59 results
Search Results
2. Memes, memetics and their applications: A systematic review of literature.
- Author
-
Rathi, Navrang and Jain, Pooja
- Subjects
MEMES ,SOCIAL media ,BRANDING (Marketing) ,POPULARITY ,SKEPTICISM - Abstract
Memes are not new concepts, but they have garnered popularity recently. They are a prevalent form of communication on various social media platforms. However, due to a lack of concrete literature (Al Rashdi, 2020), there is still some initial scepticism surrounding them. This paper uses a systematic literature review to create a pool of research papers to be examined to chart the conceptual development of memes, determine how they are used in different fields, and present a vision of memes in the near future. It identifies eleven areas which were studied in parallel to memes, suggesting possible meme applications and development. It adopts the PRISMA 2020 framework to ensure systematic screening and reporting of relevant papers from various databases. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
3. "Tears have never won anyone freedom:" a multimodal discourse analysis of Ukraine's use of memes in a propaganda war of global scale.
- Author
-
Poepsel, Mark A., Malo, Andrew, Obuekwe, Chinedu, Wilhelm, Mikayla, Perea Góngora, Valquiria, and Daiber, David
- Subjects
DISCOURSE analysis ,MEMES ,WAR ,PROPAGANDA ,MEMETICS ,FRIENDSHIP - Abstract
This paper analyzes memes posted by @Ukraine to X, formerly known as Twitter, from the account's inception in 2016 through September 2023 to examine the function and utility of memes as tools of propaganda in global conflict. A multimodal discourse analysis of more than 100 memes was conducted by a small research group to separate the memes into thematic categories in an exploratory fashion. Then, a small subset of the research group conducted a recursive analysis on the images and text in a handful of memes selected purposively to determine what types of ideological appeals were present. Our findings align with observations made in previous studies that internet memes might be used to bolster national unity and might be employed to bolster pleas for sympathy from wealthy, more powerful allies, in this case in the West. The essential propaganda ideals were those of democracy, friendship, and independence on the side of Ukraine while Russia is framed as a dictatorship. A small number of the memes referenced in our paper garnered global media attention. Though we do not make any claims about broad media effects relating to the memes studied here, garnering the attention of major U.S. publication seems clearly to have been a goal of some of these memes. For those who wish to find a key to the playbook for a nation's propaganda strategy, its social media memes are a great place to begin. It is apparent in this study that governments might use memes to cover all their propaganda bases, so to speak. Ukraine's memes appear to serve mainly to reinforce messages of national unity and messages of connection with the West. Though others have studied @Ukraine's tweets, this is believed to be the first to focus exclusively on memes posted to the feed. This study makes a significant contribution to our understanding of how wildly varied memes can communicate core concepts of a propaganda strategy, perhaps with the hope that a few will "hit" with desired media outlets, if only to bolster propaganda efforts. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
4. A Calculation and Optimization Method for the Theoretical Reclamation Timing of Cropland.
- Author
-
Yin, Hejian, Guo, Guangli, Li, Huaizhan, and Wang, Tiening
- Subjects
MINE subsidences ,FARMS ,OPTIMIZATION algorithms ,MINES & mineral resources ,WATER table ,MEMETICS ,FLOODS ,TSUNAMI damage - Abstract
In mining areas with high groundwater tables, mining subsidence can lead to the inundation of cropland by water, causing damage to cropland and posing a threat to national food security. The implementation of concurrent mining and reclamation techniques can effectively enhance the reclamation rate of cropland. This technique requires engineers to initiate reclamation measures before cropland waterlogging occurs. Therefore, when mining a panel underground, an accurate calculation of the time when cropland becomes waterlogged, known as the theoretical reclamation timing, is crucial. To address this issue, this study proposes a computational method for the theoretical reclamation timing of cropland under the conditions of single-panel mining based on intelligent optimization algorithms. In addition, this paper also proposes an optimization method for the theoretical reclamation timing of cropland within a district based on an intelligent optimization algorithm. Utilizing this method makes optimizing the layout of multiple panels possible, thereby delaying the theoretical reclamation timing for cropland within a district. This approach aims to shorten the duration of reclamation projects and minimize their interference with agricultural activities. Through experimental validation, this paper demonstrates the reliability of these two methods. This study is beneficial for the rational planning of reclamation projects. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
5. Memes, National Identity and National Belonging: Visual "Nation-Talk" on Indian Social Media Pages.
- Author
-
Neog, Krishanu Bhargav
- Subjects
SOCIAL media ,MEMES ,NATIONAL character ,HISTORY of India ,MEMETICS ,INTERTEXTUALITY - Abstract
I collected data from political social media pages to show how memes serve as means to engage in heated "nation-talk." Contending claims are made through memes on the identity of an "Indian" and on the sense of belonging to India. I collected memes over a period of twenty months from two overtly political Facebook pages with hundreds of thousands of followers. After multiple rounds of coding and filtering we arrived at a final sample of memes to be studied in depth. The memes, from opposing political sides, draw inter-textual component elements from the same popular culture, mythology etc. Through the repetition of specific components, slogans etc. these memes exclude certain claims of belonging to India while enforcing others. Key to this repetition are the circulatory dynamics of memes over digital networks, and their performative aspects. India's history emerges as a significant battleground for these memes. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
6. Binary Peacock Algorithm: A Novel Metaheuristic Approach for Feature Selection.
- Author
-
Banati, Hema, Sharma, Richa, and Yadav, Asha
- Subjects
- *
METAHEURISTIC algorithms , *FEATURE selection , *PEAFOWL , *ANIMAL sexual behavior , *LEK behavior , *MEMETICS - Abstract
Binary metaheuristic algorithms prove to be invaluable for solving binary optimization problems. This paper proposes a binary variant of the peacock algorithm (PA) for feature selection. PA, a recent metaheuristic algorithm, is built upon lekking and mating behaviors of peacocks and peahens. While designing the binary variant, two major shortcomings of PA (lek formation and offspring generation) were identified and addressed. Eight binary variants of PA are also proposed and compared over mean fitness to identify the best variant, called binary peacock algorithm (bPA). To validate bPA's performance experiments are conducted using 34 benchmark datasets and results are compared with eight well-known binary metaheuristic algorithms. The results show that bPA classifies 30 datasets with highest accuracy and extracts minimum features in 32 datasets, achieving up to 99.80% reduction in the feature subset size in the dataset with maximum features. bPA attained rank 1 in Friedman rank test over all parameters. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
7. Reinforming memes: a literature review of the status of memetic information.
- Author
-
Smith, Alexander O., Hemsley, Jeff, and Tacheva, Zhasmina Y.
- Subjects
LITERATURE reviews ,MEMES ,CITATION analysis ,MEMETICS ,RESEARCH personnel ,CULTURAL activities - Abstract
Purpose: Our purpose is to reconnect memetics to information, a persistent and unclear association. Information can contribute across a span of memetic research. Its obscurity restricts conversations about "information flow," the connections between "form" and "content," as well as many other topics. As information is involved in cultural activity, its clarification could focus memetic theories and applications. Design/methodology/approach: Our design captures theoretical nuance in memetics by considering a long standing conceptual issue in memetics: information. A systematic review of memetics is provided by making use of the term information across literature. We additionally provide a citation analysis and close readings of what "information" means within the corpus. Findings: Our initial corpus is narrowed to 128 pivotal memetic publications. From these publications, we provide a citation analysis of memetic studies. Theoretical directions of memetics in the informational context are outlined and developed. We outline two main discussion spaces, survey theoretical interests and describe where and when information is important to memetic discussion. We also find that there are continuities in goals which connect Dawkins's meme with internet meme studies. Originality/value: To our knowledge, this is the broadest, most inclusive review of memetics conducted, making use of a unique approach to studying information-oriented discourse across a corpus. In doing so, we provide information researchers areas in which they might contribute theoretical clarity in diverse memetic approaches. Additionally, we borrow the notion of "conceptual troublemakers" to contribute a corpus collection strategy which might be valuable for future literature reviews with conceptual difficulties arising from interdisciplinary study. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
8. Methodological and epistemological challenges in meme research and <italic>meme studies</italic>.
- Author
-
Galip, Idil
- Abstract
AbstractThis article examines some methodological and epistemological challenges facing
meme studies and meme research. It delves into the shifts in Anglophone meme culture post-Trump and challenges the assumption that memes are generally anonymous and antagonistic by highlighting the coexistence of collegiality and pseudonymity across diverse meme communities. Moreover, it suggests that such meme cultures can transcend from online to offline realms, requiring methodological adaptations to capture this dual dimension of creativity and sociality. The paper also addresses epistemological challenges in meme studies, starting from memetics’ contentious history and critiquing the dominance of cultural evolutionary theory in contemporary meme research. It brings attention to the academic tendency to follow a “Dawkins to Shifman pipeline” citation trope in meme research and advocates for a more critical approach informed by platform studies. It argues that the future of meme studies lies at the intersection of platform ideology and content economies, urging scholars to engage with historical and political transformations in digital culture for a comprehensive understanding of memes and their societal impact. [ABSTRACT FROM AUTHOR]- Published
- 2024
- Full Text
- View/download PDF
9. Elitism-crossover barnacle mating optimization and its application to PID controller design for a buck converter.
- Author
-
Kasruddin Nasir, Ahmad Nor, Ahmad, Mohd Redzuan, and Anwari Roslan, Muhammad Almaa
- Subjects
PID controllers ,BARNACLES ,VOLTAGE control ,STATISTICS ,MEMETICS - Abstract
This paper presents an elitism-crossover barnacle mating optimization (ECBMO). It is an improvement of barnacle mating optimization (BMO). The original BMO suffers from local optima problem leading to a low accurate solution. A new method of offspring generation is adopted into the original BMO structure. Some features of the best-so-far solution are incorporated into the generated offspring. The accuracy performance of the proposed algorithm is tested on several IEEE functions. A statistical analysis is conducted to compare its performance over the original BMO. It is also applied to optimize proportional integral derivative (PID) parameters for controlling output voltage of a buck converter. Result of benchmark functions test shows the proposed algorithm has attained higher accuracy for all functions compared to BMO algorithm. Application on the real problem shows both algorithms control the converter voltage satisfactorily. However, the ECBMO has achieved more optimal PID parameters and leading to a better output voltage response. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
10. A critical assessment of sparse PCA (research): why (one should acknowledge that) weights are not loadings.
- Author
-
Park, S., Ceulemans, E., and Van Deun, K.
- Subjects
- *
SINGULAR value decomposition , *PRINCIPAL components analysis , *MEMETICS - Abstract
Principal component analysis (PCA) is an important tool for analyzing large collections of variables. It functions both as a pre-processing tool to summarize many variables into components and as a method to reveal structure in data. Different coefficients play a central role in these two uses. One focuses on the weights when the goal is summarization, while one inspects the loadings if the goal is to reveal structure. It is well known that the solutions to the two approaches can be found by singular value decomposition; weights, loadings, and right singular vectors are mathematically equivalent. What is often overlooked, is that they are no longer equivalent in the setting of sparse PCA methods which induce zeros either in the weights or the loadings. The lack of awareness for this difference has led to questionable research practices in sparse PCA. First, in simulation studies data is generated mostly based only on structures with sparse singular vectors or sparse loadings, neglecting the structure with sparse weights. Second, reported results represent local optima as the iterative routines are often initiated with the right singular vectors. In this paper we critically re-assess sparse PCA methods by also including data generating schemes characterized by sparse weights and different initialization strategies. The results show that relying on commonly used data generating models can lead to over-optimistic conclusions. They also highlight the impact of choice between sparse weights versus sparse loadings methods and the initialization strategies. The practical consequences of this choice are illustrated with empirical datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
11. Relaxed static output feedback control for discrete-time Takagi-Sugeno fuzzy systems: A switching sequence convex optimization algorithm.
- Author
-
Gao, Jingjing, Xie, Xiangpeng, and Xia, Jianwei
- Subjects
- *
OPTIMIZATION algorithms , *FUZZY systems , *COMPLEX matrices , *MEMETICS , *MEMBERSHIP functions (Fuzzy logic) , *PROBLEM solving - Abstract
In this paper, a new switching sequence convex optimization (SSCO) algorithm is proposed for solving non-convex optimization problem with complex time-varying relaxation matrix structures that arises during output feedback design. Firstly, the introduced time-varying relaxation matrix combines the membership functions and the designed switching mechanism to adjust the positive and negative terms of the inequality constraints. As a result, relaxed controller design conditions with complex matrix structures are established. The proposed SSCO algorithm employs switching optimization variables and inner approximation strategy, which is able to compute non-convex optimization problems with complex matrix structures more flexibly and converge quickly. It is worth noting that the implementation of the SSCO algorithm requires a set of strictly feasible initial solutions. Therefore, an initialization iterative algorithm is proposed, which overcomes the difficulties of transforming the solving problem into a typical non-convex optimization problem and linearizing multiple different concave parts, by which a set of optimized feasible solutions are obtained. Finally, simulation examples are used to demonstrate the superiority of the design scheme proposed in this paper. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
12. Voronoi Tessellations based simple optimizer.
- Author
-
Bajpai, Prathu and Bansal, Jagdish Chand
- Subjects
- *
EVOLUTIONARY algorithms , *MEMETICS , *LEVY processes , *DIFFERENTIAL evolution , *COMPUTATIONAL geometry , *APPROXIMATION algorithms - Abstract
Population diversity holds significant importance in determining the success of any evolutionary algorithm. It helps the algorithm in efficiently exploring the search space and identifying the promising region(s) containing global optimal solution(s). However, during the optimization procedure the population may lose its diversity, causing premature convergence in the algorithm and resulting in approximations to the sub-optimal solution(s). This paper proposes a Voronoi Tessellations based Simple Optimizer (VTSO) algorithm that utilizes a niche concept of Voronoi Tessellations (VTs) from the field of computational geometry to ensure a well-distributed population throughout the optimization procedure. It proposed an elite sampling mechanism that utilizes Lévy flights to aggressively explore the search space for locating potential optimal region(s), and the Differential Evolution (DE) algorithm to exploit these regions in order to approximate the global optimal solution(s). In addition, a population diameter-based switch is devised which activates itself when the algorithm detects premature convergence in the algorithm. Experiments are conducted on CEC14 and CEC17 benchmark test suit, and the proposed algorithm is compared with the existing state-of-the-art evolutionary algorithms. The results are competitive to recommend the VTSO algorithm as a new efficient and accurate optimizer for handling complex optimization problems. • This paper proposed a computational geometry approach for improving population diversity in evolutionary algorithms. • This includes a novel Voronoi Tessellations-based population generation method. • A levy flight and Differential Evolution assisted approach for updating population in the search space. • In addition, a population diameter-based switch is proposed to check the stagnation or premature convergence. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
13. Evolutionary optimization of policy responses to COVID-19 pandemic via surrogate models.
- Author
-
Tayarani-N., Mohammad-H.
- Subjects
COVID-19 pandemic ,MACHINE learning ,OPTIMIZATION algorithms ,EVOLUTIONARY algorithms ,PUBLIC spaces ,MEMETICS ,VOTING - Abstract
The spread of COVID-19 has caused a great series of negative effects on countries around the world. To curb the pandemic, many governments apply a variety of strict measures including the closure of public places, transportation systems, etc. This paper proposes an algorithm that searches through a set of government policies and selects the policies that minimize the growth rate of the pandemic with minimum cost to society. The proposed algorithm first builds a surrogate model of the pandemic and then searches through the set of government policies via an evolutionary algorithm. The surrogate model in this paper is an ensemble of several learning algorithms that uses a meta-learning algorithm that estimates where in the feature space each of the learning algorithms performs better and then gives a higher weight to the learning algorithm in the voting process. Because using the surrogate models as the fitness function induces uncertainty, in this paper we use an uncertainty reduction mechanism to perform a search through the search space better. Data from 124 countries are used in this paper which contains information about the policies each government has taken since the beginning of the pandemic and 75 environmental factors like climate, religion, politics, economy, etc. Several experimental studies are performed on the output of the modeling system and the optimization algorithms and comparisons are performed with state-of-the-art methods. • A novel ensemble learning algorithm is proposed to build a model of the COVID-19 pandemic. • A list of factors that may affect the behavior of the pandemic are studied. • The best set of features for the modeling algorithm is found. • An evolutionary algorithm is proposed that optimizes the set of government policies. • An uncertainty reduction is proposed to remove approximation uncertainty. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
14. Meta-heuristic Algorithms for Double Roman Domination Problem.
- Author
-
Aggarwal, Himanshu and Reddy, P. Venkata Subba
- Subjects
METAHEURISTIC algorithms ,MEMETICS ,ANT algorithms ,GENETIC algorithms ,HEURISTIC algorithms ,RANDOM graphs - Abstract
A variety of domination concepts have been defined to provide better routing and defense strategies under different constraints. A double Roman dominating function (DROMDF) on a simple, undirected graph G is a function g : V → { 0 , 1 , 2 , 3 } such that every vertex x ∈ V with g (x) = 0 is adjacent to at least two vertices y 1 , y 2 with g (y 1) = g (y 2) = 2 or a vertex z 1 with g (z 1) = 3. Also, a vertex p with g (p) = 1 is adjacent to at least one vertex q 1 with g (q 1) ≥ 2. γ d R (G) , the double Roman domination number of G , is the smallest possible weight of all possible DROMDFs of G. Determining double Roman domination number of a graph is known to be NP-hard. Hence in this paper, we propose a genetic algorithm based approach for solving double Roman domination problem in which three heuristic algorithms have been proposed and problem specific crossover operator and a feasibility function has been developed. Further, we propose an ant colony optimization algorithm to solve double Roman domination problem. This paper provides an in-depth illustration of two algorithms for solving double Roman domination problem. Effectiveness of the proposed meta-heuristic algorithms is tested on the random graphs generated using NetworkX Erdős-Rényi model, a popular model for graph generation and Harwell–Boeing dataset, a well-known dataset for graph related problems. Further, we compare the results of both the meta-heuristic algorithms and the experimental results show that the proposed meta-heuristic algorithms for solving double Roman domination problem give a near optimal solution in reasonable time. Experimental results also show that the proposed ant colony optimization algorithm for solving double Roman domination problem outperforms genetic algorithm based procedure. • Double Roman domination problem (MDR) is NP-hard. • Proposed first ever meta-heuristic algorithms GA and ACO based solutions for MDR. • Have constructed three heuristics to generate initial population. • GA and ACO based solutions for MDR is tested on different classes of graphs. • Sign test suggests that ACO based solution for MDR outperforms GA based method. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
15. A data-driven optimisation method for a class of problems with redundant variables and indefinite objective functions.
- Author
-
Zhou, Jin, Zhou, Kang, Zhang, Gexiang, Neri, Ferrante, Shen, Wangyang, and Jin, Weiping
- Subjects
- *
OPTIMIZATION algorithms , *EVOLUTIONARY algorithms , *DOSAGE forms of drugs , *MEMETICS , *PROBLEM solving , *ALGORITHMS - Abstract
In the realm of practical problem-solving, multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) are becoming increasingly prevalent. MOPRVIF involve determining the optimal decision variables that optimise multiple objectives, leveraging the relational data of a set of variables and multiple objectives. For these problems, this paper focuses on the following two issues: one is the demand for a unified computational model to solve this problem; the other is how to improve the algorithm's deep intelligent search capability. In this regard, this paper designs a dual data-driven multi-objective optimisation method. The method used consisted of four parts: elimination of redundant variables (ERV), objective function construction (OFC), selection evolution operator (SEO), and multi-objective evolutionary algorithm (MOEA). MOEA was the main focus of the method. ERV is data preparation and variable selection according to multiple objectives. OFC involves constructing the relationship model between variables and objectives, and a high-accuracy model is important for guaranteeing reliable results. Furthermore, SEO can adjust the evolution operator during a deep search. This is an important guarantee for deep, intelligent search. MOEA combined OFC and SEO to form the final solution algorithm—Dual Data Driven Multi-Objective Evolutionary Algorithm (DDMOEA). DDMOEA was explored using two different disciplinary problems of drug compound optimisation and wild blueberry cultivation and benchmarks were selected. The first two problem domains are distinct. The first problem is more complex than the second; however, both encompass redundant variables and indefinite objective functions. Benchmarks are utilised independently to gauge the profound intelligent search capability. The experiments affirm that the dual data-driven optimization approach proposed in this paper is effective, practical, and scalable. • Provide a unified solution for optimisation problems with redundant variables and indefinite objective functions. • Propose a data-driven multi-objective optimisation algorithm. • Designing an adaptive optimiser to enhance the algorithm's depth search capability. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
16. FSBOA: feature selection using bat optimization algorithm for software fault detection.
- Author
-
Pethe, Yoginee Surendra, Gourisaria, Mahendra Kumar, Singh, Pradeep Kumar, and Das, Himansu
- Subjects
OPTIMIZATION algorithms ,MEMETICS ,ANT algorithms ,FEATURE selection ,PARTICLE swarm optimization ,SOFTWARE measurement - Abstract
Feature selection (FS) plays a crucial role in software fault prediction (SFP), aiming to identify a subset of relevant and discriminative features from a large pool of software metrics. It serves as a critical preprocessing step in building accurate fault prediction models, enabling the identification of potential software faults early in the development life cycle and facilitating effective resource allocation for testing and maintenance activities. The study's objective is to determine how well the bat optimization algorithm (BOA) can extract the features that are most important for correctly predicting software flaws, improve the accuracy of fault prediction, reduce the dimensionality of the feature space, and mitigate the risk of overfitting, thereby enabling more effective resource utilization and better allocation of testing efforts. The forecasting models underwent testing and training utilizing a collection of software metrics, with the datasets undergoing evaluation using several different FS algorithms. An assessment was conducted by contrasting the effectiveness of multiple optimization algorithms, including evolutionary methods such as FS employing genetic algorithm (FSGA), FS employing differential evolution (FSDE), and swarm-based techniques such as FS employing ant colony optimization (FSACO), FS employing particle swarm optimization (FSPSO), FS employing firefly algorithm (FSFA), and FS employing binary grey wolf optimization algorithm (FSBGWO) in relation to FS employing bat optimization algorithm (FSBAO). The results obtained from FSBAO approach demonstrate the effectiveness in solving FS optimization problems with at most accuracy of 98.92%. Furthermore, the experimental results have been statistically validated for the greater efficiency of the proposed FSBAO algorithm. This study's findings have crucial implications for developing a software failure prediction models that is more accurate and efficient. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
17. A staged diversity enhancement method for constrained multiobjective evolutionary optimization.
- Author
-
Yu, Fan, Chen, Qun, Zhou, Jinlong, and Li, Yange
- Subjects
- *
OPTIMIZATION algorithms , *EVOLUTIONARY algorithms , *CONSTRAINED optimization , *SOCIAL dominance , *MEMETICS - Abstract
Optimizing the convergence and diversity of solutions simultaneously under constraints is a challenge in solving constrained multiobjective optimization problems. In existing multiobjective optimization algorithms, general diversity maintenance mechanisms have difficulty determining all optimal solutions in discrete feasible regions. This paper proposes a staged constrained multiobjective optimization algorithm with a diversity enhancement method (SDEM), which can explore potential discrete feasible regions by retaining well-distributed offspring. Specifically, after solutions have converged to optimal feasible regions by niching-based constraint dominance in the early stage, the SDEM improves the diversity of solutions through a proposed diversity enhancement dominance principle in the mid-term. Finally, the optimize objective functions and constraints of all solutions are optimized under constraint dominance to balance convergence, diversity, and feasibility during the three stages. Experiments on four well-known test suites and six real-world case studies demonstrate that the SDEM is competitive with or comparable to seven state-of-the-art constrained multiobjective evolutionary algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
18. Manifold neighboring envelope sample generation mechanism for imbalanced ensemble classification.
- Author
-
Wang, Yiwen, Li, Yongming, Shen, Yinghua, Li, Fan, and Wang, Pin
- Subjects
- *
MEMETICS , *CLUSTER sampling , *PROBLEM solving , *CLASSIFICATION - Abstract
For existing imbalanced ensemble (IE) methods, the sample subsets are constructed from the same dataset, which usually suffer from low quality (diversity and separability) of the subsets, so a manifold neighboring envelope sample generation mechanism (MNESG) and an imbalanced ensemble algorithm based on the mechanism (MNESG-IE) are proposed to solve this problem. First, for the original balanced subsets (OBS), a manifold neighboring sample envelope projection mechanism (MNSEP) is designed to mine the local correlation information between the samples and their nearest neighbors in the subsets. Second, the fuzzy c-means clustering (FCM) is used to further mine the global correlation information among similar samples in the subsets. Third, the sample distribution consistency preservation mechanism (SDCPM) is designed to enhance the consistency of the sample distribution before and after clustering. To better reduce the three accumulated losses above, the three steps are conducted simultaneously, thereby realizing the MNESG, which can transform the OBS into two new types of high quality envelope sample subsets − neighboring envelope sample (NES) subsets and neighboring cluster envelope sample (NCES) subsets. Finally, base classifiers are trained on the NES subsets and NCES subsets, and then fused by a two-dimensional sparse fusion mechanism (2D-SFM). Various representative IE algorithms on over thirty benchmark datasets are considered for verification. The results show that compared with the state-of-the-art IE algorithms, MNESG-IE achieves 17.79%, 17.90%, 23.61%, 18.08% improvement in terms of ACC, AUC, F-M and G-M, respectively. The major originality of the paper is: (a) proposing the MNSEP to mine the local correlation information for improving the quality of the subsets; (b) proposing the MNESG to generate high quality subsets by mining local and global correlation information simultaneously; and (c)forming an IE algorithm to better solve the imbalanced classification problem. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
19. A Pacesetter-Lévy multi-objective particle swarm optimization with Arnold Chaotic Map with opposition-based learning.
- Author
-
Kang, LanLan, Lai, Yu, Wang, Jia, and Cao, WenLiang
- Subjects
- *
PARTICLE swarm optimization , *OPTIMIZATION algorithms , *MACHINE learning , *MEMETICS , *GREY relational analysis , *K-means clustering , *EVOLUTIONARY algorithms - Abstract
To accelerate the convergence of multi-objective optimization algorithm and achieve an optimization solution set with good diversity, this paper proposes the Pacesetter-Lévy Multi-Objective Particle Swarm Optimization using Arnold Chaotic Map with Opposition-Based Learning algorithm (PLMOPSOCO). Firstly, the Arnold Chaotic Map with Opposition-based Learning is proposed to generate some valuable particles in the stage of initialization while maintaining the diversity of population, which can speed up exploration. Secondly, a competition mechanism with k-means clustering is integrated to categorize particles into losers and winners. The flight direction of the loser particles is adjusted according to the Pacesetter-Lévy kinetic equation, an innovative approach to effectively enhance the population's exploitative ability. Thirdly, a crossover mutation is implemented to assist particles to escape local optima and prevent population stagnation. Comparative studies on benchmark functions demonstrate that this new algorithm is more competitive in terms of both diversity and convergence when compared to several advanced multi-objective algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
20. A dynamic multi-objective evolutionary algorithm based on Mahalanobis distance and intra-cluster individual correlation rectification.
- Author
-
Ge, Fangzhen, Hou, Xing, Chen, Debao, Shen, Longfeng, and Liu, Huaiyu
- Subjects
- *
EVOLUTIONARY algorithms , *MEMETICS , *STATISTICAL correlation , *TIME series analysis , *RANDOM noise theory , *COMPETITIVE advantage in business - Abstract
Responding quickly and accurately to environmental changes is a challenge in addressing dynamic multi-objective optimization problems (DMOPs). Although many dynamic multi-objective evolutionary algorithms (DMOEAs) have demonstrated impressive performance, there is still room for improvement in accurately predicting population behavior. To address this limitation, this paper proposes a prediction strategy based on Mahalanobis distance and intra-cluster individual correlation rectification (MCIR) to deal with DMOPs. First, a manifold clustering method is used to partition the Pareto set of the population into subpopulations, in which individuals with similar movement trends are grouped into clusters. Second, the Mahalanobis distance is introduced to systematically measure the relationships between clusters in adjacent environments. Time series models are established for each cluster center to predict their positions in the neighboring environment. On this basis, the movement characteristics of individuals within clusters are further rectified by calculating the correlation between intra-cluster individuals and the cluster center, facilitating more accurate tracking of the changing Pareto set/Pareto front. Finally, Gaussian noise is introduced to ensure the diversity of new individuals. The effectiveness of the MCIR algorithm is demonstrated by comparing it with four DMOEAs using 18 test instances. Experimental results confirm that MCIR holds great promise in addressing DMOPs. • An effective strategy based on Mahalanobis distance is proposed for constructing time series in dynamic multi-objective optimization problems. • An incremental rectification strategy using correlation analysis is introduced to estimate individual movement trends accurately. • Comparative results on DMOP test suites show the algorithm has a distinct competitive advantage and potential over others. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
21. An adaptive evolutionary search-based method for efficiently tackling the set-union knapsack problem.
- Author
-
Zhao, Juntao and Hifi, Mhand
- Subjects
- *
KNAPSACK problems , *HAMMING distance , *DATABASES , *EVOLUTIONARY algorithms , *BACKPACKS , *MEMETICS - Abstract
This paper tackles the NP-hard set-union knapsack problem using a novel adaptive evolutionary algorithm. Such a problem has applications in database query optimization, network design, and project portfolio management. The method starts by creating an archive set with elite and diversified solutions, employing a customized procedure based on item scores and Hamming max-min distance for diversity. Various operators are added to enhance solution quality in the elite set and refine the search process. To counteract premature convergence, combination and subset reference update methods are used. The fusion strategy serves as an exploration mechanism, generating new solutions and preventing premature convergence. Finally, the effectiveness and performance of the proposed method is evaluated on a set of benchmark instances taken from existing literature and by conducting a thorough statistical analysis of all achieved results. All obtained results are systematically compared against those achieved by state-of-the-art methods, indicating a substantial improvement of 50% for the large-sized instances, outperforming those published in the literature. • An adaptive evolutionary method for the set-union knapsack is designed. • The new method collaborates elite and diversified solutions for enhanced performance. • An exploitation process is introduced to enhance the quality of elite solutions. • The search space is explored using a fusion operator with Hamming max-min distance. • The new method achieves 50% improvement over published solutions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
22. A novel cooperative swarm intelligence feature selection method for hybrid data based on fuzzy β covering and fuzzy self-information.
- Author
-
Li, Zhaowen, Cai, Xiaopeng, Huang, Qin, and Lin, Yonghua
- Subjects
- *
SWARM intelligence , *FEATURE selection , *PARTICLE swarm optimization , *MEMETICS , *MULTIPLE criteria decision making , *GROUP decision making , *HEURISTIC , *SEARCH algorithms - Abstract
As the generalization of fuzzy covering, fuzzy β covering can effectively deal with uncertain information in hybrid data. Swarm intelligence algorithm has unique advantages in feature selection. This paper proposes a novel cooperative swarm intelligence feature selection method for hybrid data based on fuzzy β covering and fuzzy self-information. First, the parameterized fuzzy β neighborhood and fuzzy decision in a fuzzy β covering decision information system are defined, and the lower and upper fuzzy approximations are further studied. Fuzzy rough set model exclusively focuses on the information derived from the lower fuzzy approximation within a decision. In actual circumstances, the uncertainty of fuzzy information is linked to not only the lower fuzzy approximation but also the upper fuzzy approximation. Then, by incorporating the lower and upper fuzzy approximations of a decision, fuzzy self-information and relative fuzzy self-information are introduced as two evaluation indicators of feature selection. Next, a forward feature selection algorithm based on heuristic search strategy is designed by means of these two evaluation indicators. Moreover, in order to effectively explore feature subsets to a greater extent, a cooperative swarm intelligent feature selection algorithm based on random search strategy is also designed by combining particle swarm optimization algorithm and cuckoo search algorithm. Finally, an array of experiments is executed to compare two designed algorithms with five other existing feature selection algorithms. Experimental results show that these two designed algorithms achieve a high feature selection rate while improving the classification accuracy of the raw data. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
23. A density clustering-based differential evolution algorithm for solving nonlinear equation systems.
- Author
-
Guo, Yan, Li, Mu, Jin, Jie, and He, Xianke
- Subjects
- *
DIFFERENTIAL evolution , *NONLINEAR systems , *MEMETICS , *ALGORITHMS , *PROBLEM solving , *DENSITY , *ARCHIVES , *WEB archives - Abstract
Solving nonlinear equation systems (NESs) is one of the most important tasks in numerical computing. The NESs usually have multiple roots, and quickly locating their roots in a single algorithm run with a limited number of iterations has always been the algorithm improvement direction. In order to further enhance the efficiency of the existing methods, a density clustering-based differential evolution algorithm (DCDE) for the NESs problem solving is proposed in this paper. Firstly, density-based spatial clustering of applications with noise (DBSCAN) is used to divide the population into clusters and noises, which provides a better direction for population evolution. Secondly, a cluster evaluation factor is proposed, which not only divides the clusters into excellent clusters and non-excellent clusters, but also prevents the migration of excellent clusters and maintain the diversity of populations. Then, a migration strategy and individual generation mechanism are proposed to guide non-excellent clusters to migrate to promising regions. Finally, combined with an archive technique, the individuals which satisfy the solution requirements are archived and randomly initialized to further improve population diversity. To verify the effectiveness of the proposed algorithm, comparative experimental results of the propsoed DCDE with other state-of-the-art algorithms for thirty NESs problems solving show that the proposed DCDE is the most effective algorithm to locate more roots in a single run. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
24. A two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization.
- Author
-
Zou, Juan, Tang, Li, Liu, Yuan, Yang, Shengxiang, and Wang, Shiting
- Subjects
- *
EVOLUTIONARY algorithms , *MEMETICS , *PARETO optimum , *HOTEL suites - Abstract
Large-scale multiobjective optimization problems (LSMOPs) have exponential growth in the search space as the decision variables increase, and the vast search space poses a challenge to the performance of multiobjective evolutionary algorithms (MOEAs). Many current large-scale MOEAs need to consume a large amount of computational resources to get good performance. This paper proposes a two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization (LMOEA-S2D) to balance the performance and computational resource overhead. The algorithm exploits the Pareto-optimality property of domination and the diversity-preserving property of decomposition to optimize the performance in the two stages, respectively, and designs a corresponding direction-guided mechanism to improve search efficiency. LMOEA-S2D designs global direction search and local direction search in the domination-based stage for efficient exploitation to accelerate population convergence. To promote greater population diversity, a hybrid direction search was devised to aid diversity exploration in the decomposition-based stage, and this facilitates even distribution of candidate solutions across the Pareto optimal frontier. LMOEA-S2D is compared with five state-of-the-art large-scale MOEAs on some large-scale multiobjective test suites with 100 to 5,000 decision variables. The experimental results show that LMOEA-S2D significantly outperformed all compared algorithms under limited computational resources. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
25. On the Experience of Statistical Processing of Memes in Big Data Format
- Author
-
Potapova, Rodmonga, Potapov, Vsevolod, Gorbunov, Petr, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Yang, Xin-She, editor, Sherratt, Simon, editor, Dey, Nilanjan, editor, and Joshi, Amit, editor
- Published
- 2024
- Full Text
- View/download PDF
26. Joint resource allocation and UAV placement in UAV-assisted Wireless Powered Sensor Networks using TDMA and NOMA.
- Author
-
Azarhava, Hosein, Abdollahi, Mehran Pourmohammad, Niya, Javad Musevi, and Tinati, Mohammad Ali
- Subjects
WIRELESS sensor networks ,RESOURCE allocation ,NEXT generation networks ,ENERGY harvesting ,MEMETICS ,KNOWLEDGE transfer ,INPAINTING - Abstract
Widely employment of UAV-assisted communications along with the energy harvesting technology in the next generation of telecommunication networks, is one of the promising solutions to provide improved metrics whereas it brings new challenges. In this paper, we consider UAV-assisted Wireless Powered Sensor Network (WPSN) in which the sensors are distributed in the area and harvest their energy from the UAV above, during downlink wireless energy transmission (DL-WET) phase and transmit the gathered data to the UAV during uplink wireless information transfer (UP-WIT) phase under TDMA and NOMA protocols. The total uplink sum-rate for both protocols are calculated, and then, the joint resource allocation and UAV placement optimization problems are defined over time schedule and power parameters under minimum data rate and energy constraints. The optimization problems turns out to be non-convex and intractable to be solved. Therefore, we separate the problems into two subproblems which could be solved through regular methods. An iterative algorithm with fast convergency characteristic is proposed to obtain the suboptimal solutions for the optimization problems. Comparing the numerical results for the NOMA and TDMA approaches shows that in a power limited scenario, NOMA-based approach outperforms the uniform resource allocation while it is as the same as TDMA-based one. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
27. SAC-based UAV mobile edge computing for energy minimization and secure data transmission.
- Author
-
Zhao, Xu, Zhao, Tianhao, Wang, Feiyu, Wu, Yichuan, and Li, Maozhen
- Subjects
MEMETICS ,DEEP reinforcement learning ,MOBILE computing ,REINFORCEMENT learning ,DATA transmission systems ,EDGE computing - Abstract
In recent years, the use of UAVs to carry mobile edge computing servers has become an emerging solution to the problem of collaborative ground-to-air communication. However, due to the limited energy consumption of UAVs and the open nature of wireless communication, UAV-based mobile edge computing faces issues such as limited operation time and the potential for insecure connections during data transmission. In this paper, we model a secure data transmission model for UAVs with the goal of minimizing energy consumption. First, we decompose the system energy consumption optimization problem into two sub-problems: ground user energy consumption overhead and UAV energy consumption overhead. We consider the limitations of UAV flight range, computational power, and transmission capability in relation to ground user and UAV, solve the energy consumption optimization problem by deep reinforcement learning, and propose a solution based on the SAC algorithm. The solution applies the idea of maximum entropy to explore the optimal policy and use efficient iterative updates to obtain the optimal policy, enhancing the exploration capability of the algorithm and improving the convergence speed of the training process by retaining all policies with high return values. Secondly, considering the existence of insecure connection nodes in practical application scenarios, a ground user data protection scheme based on Paillier encryption and blockchain is used to achieve secure data transmission. Simulation results show that the proposed method can achieve secure data offloading, effectively reduce the average energy consumption of users, and have good stability and convergence compared with existing methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
28. 'Who is /ourguy/?': Tracing panoramic memes to study the collectivity of 4chan/pol/.
- Author
-
Hagen, Sal
- Subjects
MEMETICS ,ACTOR-network theory ,MEMES ,TEXT mining - Abstract
This article explores how Internet memes can be traced as nodal points for the study of online groups. Such 'meme tracing' is specifically pertinent to the study of anonymous imageboards like 4chan, where inquiry cannot be easily based on the individual. Drawing from actor-network theory, I argue 'panoramic memes' – memes that repeatedly paint a totalising picture of a collective – are especially useful to identify what narratives hold such anonymous groups together. To operationalise this, I conducted a qualitative-quantitative case study of '/ourguy/': a meme used to suggest a certain public figure is representative of the beliefs of an entire group. Using text mining methods, I traced this term to the names of public figures on 4chan's far-right /pol/ board. This reveals that Donald Trump and Robert Mueller were most commonly proposed as an '/ourguy/' between 2016 and 2020, while the meme was entangled with conspiracy creation and far-right mobilisation. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
29. Res Cogitans – The Evolution of Thinking
- Author
-
Lindenfors, Patrik
- Published
- 2024
- Full Text
- View/download PDF
30. AHA-3WKM: The optimization of K-means with three-way clustering and artificial hummingbird algorithm.
- Author
-
Chen, Xiying, Liu, Caihui, Lin, Bowen, Lai, Jianying, and Miao, Duoqian
- Subjects
- *
K-means clustering , *HUMMINGBIRDS , *DATA mining , *MEMETICS , *ALGORITHMS , *BIRD behavior , *MACHINE learning - Abstract
Clustering, as an essential technique in unsupervised learning, plays a pivotal role in the fields of data mining and machine learning. However, the classic K -means clustering algorithm has intrinsic drawbacks such as sensitivity to initial cluster centers, susceptibility to a local optimal solution, and challenges in handling data uncertainty. To address these problems, this paper proposes an artificial hummingbird algorithm (AHA)-based three-way K -means clustering algorithm, called AHA-3WKM. First, AHA is introduced to address the problems of sensitivity to initial cluster centers and local optima. Second, a fitness function of AHA is specifically constructed to find the best initial clustering centers so that the hummingbirds can search for high-quality food sources, i.e., the global optimum cluster centers. Third, a three-way clustering approach is utilized to capture information about data uncertainty. In this way, the results of clustering are divided into three distinct regions based on the relationship between objects and clusters. The experimental results demonstrate that AHA-3WKM has good performance, and enhances the stability and the accuracy of clustering results. • AHA is introduced to address the problems of the sensitivity to initial cluster centers and the proneness to local optima. Hummingbirds are treated as data points, which dynamically update their strategies and effectively find the optimal cluster centers during multiple iterations. • A fitness function is designed based on the clustering principle of "birds of a feather flock together", with the aim of simplifying calculations, which enhances the specificity and practicality of K-means algorithm. • An AHA-based three-way K-means clustering algorithm (i.e., AHA-3WKM) is proposed. The clustering process is initialized with cluster centers optimized by AHA, and the results are represented in three regions, which can capture the uncertainty within the datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
31. A convolutional neural network based on an evolutionary algorithm and its application.
- Author
-
Zhang, Yufei, Wang, Limin, Zhao, Jianping, Han, Xuming, Wu, Honggang, Li, Mingyang, and Deveci, Muhammet
- Subjects
- *
CONVOLUTIONAL neural networks , *EVOLUTIONARY algorithms , *AIR pollution control , *TIME complexity , *LOGICAL prediction , *MEMETICS , *ELECTRONIC information resource searching , *AIR quality - Abstract
• First introduced PD control theory for optimizing learning rate in CNN models. • Designed of innovative center selection and elite guidance strategies. • Developed an efficient enhanced battle royale algorithm(EBRO). • Proposed a new EBRO-ICNN model for air quality prediction. PM 2.5 concentration predictions can provide air pollution control, management, and early warning. However, the PM 2.5 data with high-dimensionality, complexity, and dynamics pose a great challenge to achieve optimal prediction results. Convolutional Neural Networks (CNNs) has unique advantages in processing complex data and contributes to state-of-the-art performances. However, designing the architecture and selecting the learning rate for CNN are time-consuming and requires prior knowledge. Evolutionary algorithms, with the advantages of global convergence, ergodicity, robustness and adaptability, are the most commonly used methods to design the optimal framework for CNNs. Therefore, to improve the predictive performance of CNNs, this paper proposes an improved CNN method (EBRO-ICNN) which employs the enhanced battle royale optimization (EBRO) algorithm and proportional-derivative (PD) control. Firstly, the EBRO algorithm with multistrategy collaborative optimization is introduced, and validated by CEC2017 benchmark functions, which demonstrates EBRO strong global exploration capability, fast convergence speed, and low time complexity. Next, PD control is applied to adjust the learning rate of CNN (ICNN) dynamically, which improves the efficiency and stability of the network training process. At the same time, the ICNN model is optimized using the EBRO algorithm, which can reduce the human interference, generate the optimal framework automatically and enhance the prediction accuracy effectively. Finally, a private air quality dataset and two public datasets are utilized to evaluate the performance of the EBRO-ICNN model, considering 3 error evaluation metrics and 7 prediction comparison models. The experimental results demonstrate that the EBRO-ICNN model exhibits great accuracy and stability. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
32. Accurate parameters extraction of photovoltaic models with multi-strategy gaining-sharing knowledge-based algorithm.
- Author
-
Xiong, Guojiang, Gu, Zaiyu, Mohamed, Ali Wagdy, Bouchekara, Houssem R.E.H., and Suganthan, Ponnuthurai Nagaratnam
- Subjects
- *
MEMETICS , *OPTIMIZATION algorithms , *DIFFERENTIAL evolution , *ALGORITHMS , *PHOTOVOLTAIC power systems , *SEARCH algorithms - Abstract
• An improved gaining-sharing knowledge-based algorithm named MSGSK is proposed. • A parameter adjustment strategy is developed to adjust relative parameters of MSGSK. • A backtracking differential mutation strategy is designed to enrich the population diversity. • A strategy selection mechanism is presented to integrate the former two strategies. • MSGSK extracts more accurate parameters for five PV models. The determination of photovoltaic (PV) model parameters has essential theoretical and practical significance for the performance evaluation, power monitoring, and power generation efficiency calculation of PV systems. In this paper, a multi-strategy gaining-sharing knowledge-based algorithm (MSGSK) is developed to determine these parameters. In our previous work, it has been demonstrated that gaining-sharing knowledge-based algorithm (GSK) is well suited for solving the concerned problem. To enhance its performance, a parameter adjustment strategy is developed to adjust the knowledge rate and knowledge ratio of GSK. Besides, a backtracking differential mutation strategy by combining the mutation scheme of differential evolution and the updating scheme of backtracking search optimization algorithm is developed to enrich the population diversity. Furthermore, a strategy selection mechanism is introduced to integrate the former two strategies to balance exploration and exploitation in different stages of the evolutionary process. The suggested MSGSK algorithm is applied to five PV cases (SDM, DDM, Photowatt-PW201, STM6-40/36, and STP6-120/36). From the experimental data, it can be observed that MSGSK extracts the PV model parameters more precisely than the basic GSK. Furthermore, it exhibits faster convergence speed and higher accuracy compared to other advanced algorithms found in the literature. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
33. Developing a hybrid system for stock selection and portfolio optimization with many-objective optimization based on deep learning and improved NSGA-III.
- Author
-
Lv, Mengzheng, Wang, Jianzhou, Wang, Shuai, Gao, Jialu, and Guo, Honggang
- Subjects
- *
HYBRID systems , *PORTFOLIO management (Investments) , *CONVOLUTIONAL neural networks , *DEEP learning , *MEMETICS , *RECURRENT neural networks - Abstract
• Equity value and debt maturity make this portfolio more complete by using KMV model. • The objectives of the proposed portfolio include order moments and default distance. • Improved NSGA-III with chaos map is developed to solve five-objective optimization. • Static penalty is transformed the constrained optimization to unconstrained problem. • CNN-BiLSTM is firstly used to predict trend when selecting stocks. Portfolio management is a critical aspect of investment strategies, with the goal to balance the low-risk and high-return investments. Despite this, existing portfolios frequently overlook the integration of stock selection outcomes and underutilize data from listed companies, leading to suboptimal portfolio performance. Addressing these shortcomings, this paper introduces a hybrid system involving stock selection and portfolio optimization. In stock selection, the system employs a combination of convolutional neural network and bi-directional recurrent neural network to predict stock trends. This approach enables the identification of stocks likely to appreciate in value, setting the stage for their inclusion in the subsequent optimization process. For portfolio optimization, the study formulates a five-objective optimization problem that incorporates mean, variance, skewness, kurtosis, and distance-to-default as key considerations. To solve the many-objective constrained optimization problem, an advanced strategy employing a static penalty function and an improved Non-dominated Sorting Genetic Algorithm III (NSGA-III) based on tent chaotic mapping is utilized. The efficacy of the proposed hybrid system is rigorously tested through three sets of ablation experiments alongside two discussions focused on its robustness and computational efficiency. The findings from these investigations reveal that the hybrid system outperforms traditional approaches, reducing risks and improving returns for investors. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
34. Many-objective evolutionary self-knowledge distillation with adaptive branch fusion method.
- Author
-
Bai, Jiayuan and Zhang, Yi
- Subjects
- *
OPTIMIZATION algorithms , *TECHNOLOGICAL innovations , *EVOLUTIONARY algorithms , *TRADITIONAL knowledge , *TEACHER training , *MEMETICS , *ECCENTRIC loads , *GREY relational analysis - Abstract
As a new technology for model compression, self-knowledge distillation (SKD) avoids the large computational overhead of training the teacher model seen with traditional knowledge distillation. However, existing SKD methods pay attention to the knowledge transfer between deep and shallow layers of the network but ignore the mutual learning between shallow branches. This paper proposes a many-objective evolutionary self-knowledge distillation framework (MaOESKD) to guide the knowledge fusion between branches in the SKD neural network. This framework embeds an optimization module and a temporary branch into the multi-branch SKD network. The optimization module includes a many-objective adaptive weight optimization model (MaAWOM) and an many evolutionary optimization algorithm based on multi-strategy consensus mechanism (MaOEA-MCM); meanwhile, the temporary branch performs linear weighted fusion. In the MOBWOM, the weight of different branches in knowledge fusion is taken as the decision variable, and the mutual information, covariance, KL divergence between branch output features, and the total information of each branch are taken as the optimization objective. The MSCMEA integrates several state-of-the-art individual selection strategies in the field of evolutionary algorithms. It includes shift density estimation (SDE), penalized boundary intersection (PBI), balanced fitness estimation (BFE), and adaptive position transformation (APT). The accuracy of MOESKD achieves 99.70, 95.74 and 78.21 in MNIST, CIFAR-10 and CIFAR-100. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
35. Multi-objective evolutionary algorithm with evolutionary-status-driven environmental selection.
- Author
-
Lin, Kangnian, Li, Genghui, Li, Qingyan, Wang, Zhenkun, Ishibuchi, Hisao, and Zhang, Hu
- Subjects
- *
EVOLUTIONARY algorithms , *GAUSSIAN mixture models , *SOCIAL dominance , *CROWDS , *BENCHMARK problems (Computer science) , *MEMETICS , *OUTLIER detection , *SOURCE code - Abstract
The hardly dominated boundary (HDB) is a common feature of multi-objective optimization problems (MOPs). Previous studies have proposed several multi-objective evolutionary algorithms (MOEAs) to deal with the problem characterized by HDBs. Nevertheless, these methods lack consideration of the evolutionary status, potentially resulting in low efficiency when addressing problems that entail a confluence of HDB and other intricate characteristics. This paper proposes an MOEA with evolutionary-status-driven environmental selection (MOEA-ESD) to address such an issue. Specifically, in each generation, the current and historical information are used to evaluate the evolutionary status. Based on the estimated evolutionary status, the proposed MOEA-ESD algorithm adaptively uses three types of environmental selection: the traditional environmental selection (TES) based on Pareto-dominance and crowding distance, a convergence-first environmental selection (CFES) based on Gaussian mixture model clustering, and a diversity-first environmental selection (DFES) based on outlier detection and extreme solutions. In this way, inferior solutions situated on the HDB can be effectively eliminated, thereby facilitating the convergence of the population while concurrently preserving a commendable degree of diversity. Moreover, a set of new benchmark problems with different objective magnitudes and complicated Pareto sets is developed to enrich the features of HDB-MOPs and to verify the algorithm's performance. Our experimental results on 22 HDB-MOPs show the promising performance of the proposed algorithm. The source code of MOEA-ESD is available at https://github.com/CIAM-Group/EvolutionaryAlgorithm_Codes/tree/main/MOEA-ESD. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
36. Sparse K-means clustering algorithm with anchor graph regularization.
- Author
-
Yang, Xiaojun, Zhao, Weihao, Xu, Yuxiong, Wang, Chang-Dong, Li, Bin, and Nie, Feiping
- Subjects
- *
K-means clustering , *GRAPH algorithms , *OPTIMIZATION algorithms , *DATA distribution , *MEMETICS , *COMPUTATIONAL complexity - Abstract
As a classical unsupervised learning method, the K-means algorithm selects the cluster centers randomly and calculates the mean values of the cluster's data points to generate clusters. However, its performance is susceptible to the initial cluster centers and the sparsity of the membership matrix. To overcome these limitations, in this paper, we propose a sparse K-means clustering algorithm with anchor graph regularization (SKM-AGR) for optimizing initial cluster center sensitivity and improving membership matrix sparsity. The main idea is to use the anchor graph regularization (AGR) constrained K-means models, which effectively learn the membership matrix of data points and the membership matrix of anchors. In particular, by constructing an anchor graph, the AGR term not only discovers the internal structure information of data, but also covers the data distribution. Furthermore, an alternating optimization algorithm with fast-converging is adopted to solve the optimization problems of SKM-AGR, and the computational complexity is analyzed. Extensive clustering experiments on several synthetic and benchmark datasets show that the proposed SKM-AGR method performs better than several previous methods in most cases. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
37. A large-scale graph partition algorithm with redundant multi-order neighbor vertex storage.
- Author
-
Cui, Huanqing, Yang, Di, and Zhou, Chuanai
- Subjects
- *
GRAPH algorithms , *PARALLEL algorithms , *DISTRIBUTED computing , *GENETIC algorithms , *COMPUTER systems , *MEMETICS - Abstract
Recently, graph data become increasingly important and larger in many fields, and many distributed graph computing systems have been proposed to deal with large-scale graphs. The graph partition algorithm is the basis of these systems. In many graph applications, a vertex updates its feature by aggregating information of its multi-order neighboring vertices, and the existing graph partition algorithms often lead to heavy communication cost. This paper proposes a graph partition algorithm to store the multi-order neighbor vertices redundantly to avoid the communication requirement. It first formulates the problem of graph partitioning with redundant multi-order neighbor vertices storage as an optimization problem, and then proposes PARN (partition algorithm with redundant multi-order neighbor) algorithm to tackle it. The PARN algorithm consists of initial partition and vertex migration based on genetic algorithm, and it classifies the vertices of a partition into primary and auxiliary vertices. The auxiliary vertices required for the primary vertices in a partition are stored in the same partition, so the primary vertices don't need to fetch the information from the other partitions. The experiment results show that the proposed algorithm is comparable to Hash, GAP, GNP, and QCLP algorithms in terms of load balance and produces fewer auxiliary vertices than these algorithms. • Formulating the graph partitioning problem with redundant storage of multi-order neighbor vertices. • Presenting a graph partitioning algorithm PARN to solve this problem. • Performing extensive experiments to show the out-performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
38. Balancing Pareto Front exploration of Non-dominated Tournament Genetic Algorithm (B-NTGA) in solving multi-objective NP-hard problems with constraints.
- Author
-
Antkiewicz, Michał and Myszkowski, Paweł B.
- Subjects
- *
NP-hard problems , *GENETIC algorithms , *TOURNAMENTS , *COMBINATORIAL optimization , *EVOLUTIONARY computation , *ARCHIVES , *MEMETICS - Abstract
The paper presents a new balanced selection operator applied to the proposed Balanced Non-dominated Tournament Genetic Algorithm (B-NTGA) that actively uses archive to solve multi- and many-objective NP-hard combinatorial optimization problems with constraints. The primary motivation is to make B-NTGA more efficient in exploring Pareto Front Approximation (PFa), focusing on "gaps" and reducing some PFa regions' sampling too frequently. Such a balancing mechanism allows B-NTGA to be more adaptive and focus on less explored PFa regions. The proposed B-NTGA is investigated on two benchmark multi- and many-objective optimization real-world problems, like Thief Traveling Problem and Multi-Skill Resource-Constrained Project Scheduling Problem. The results of experiments show that B-NTGA has a higher efficiency and better performance than state-of-the-art methods. • New balanced GAP selection actively uses archive, focuses on Pareto Front 'gaps' to reduce too frequent sampling of regions. • B-NTGA bases and modifies NTGA2 (i.e., clone elimination, tournament as second selection) and adds balanced GAP selection. • B-NTGA is less time-consuming than NTGA2, uses fewer parameters, and is more efficient than NTGA2. • Experiments for NP-hard multi-objective optimization problems (TTP and MS-RCPSP) comparing B-NTGA to state-of-the-art methods. • Results of experiments showed that B-NTGA is highly effective for multi- and many-objective problems (like TTP and MS-RCPSP). [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
39. MRI-CE: Minimal rare itemset discovery using the cross-entropy method.
- Author
-
Song, Wei, Sun, Zhen, Fournier-Viger, Philippe, and Wu, Youxi
- Subjects
- *
CROSS-entropy method , *BLACK swan theory , *HEURISTIC , *ASSOCIATION rule mining , *MEMETICS , *SAMPLE size (Statistics) , *PERFORMANCES - Abstract
Rare itemsets have been studied less extensively than frequent itemsets, but have important potential applications in black swan events, like detecting anomalies. Mining rare itemsets poses two challenges: too many results may be obtained, and the process may incur a high computational overhead. To overcome these two challenges, we can attempt to mine minimal rare itemsets (MRIs) and use heuristic methods to mine approximate results instead of exact results. This paper describes a novel algorithm for mining MRIs using cross-entropy (CE). We present the modeling method for MRI-CE and introduce a progressive checking strategy that enables more MRIs to be discovered in each iteration. The discovered MRIs are then used to update a probability vector. We design two optimization strategies to improve the algorithm's performance. The adaptive sample size strategy narrows the search space as the number of iterations increases, and the crossover-based individual generation strategy improves the diversity of the samples. To evaluate the performance of MRI-CE, we select six competitive algorithms and conduct extensive experiments on publicly available datasets. The results show that the proposed algorithm is not only efficient, but also highly accurate. Furthermore, we verify the effectiveness of the two optimization strategies experimentally. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
40. Differential evolution with proration-based mutation strategy and multi-segment mixed parameter setting for numerical optimization.
- Author
-
Yan, Xueqing, Tian, Mengnan, and Li, Yongming
- Subjects
- *
DIFFERENTIAL evolution , *MEMETICS , *SEARCH algorithms , *GLOBAL optimization , *ALGORITHMS - Abstract
In this paper, a new differential evolution (DE) algorithm is presented for solving global optimization problems by guiding the individuals adaptively to explore different decision regions and setting the control parameters properly for individuals. To consolidate algorithm efficiency and preserve population diversity, we first introduce a proration-based mutation strategy. This strategy dynamically selects promising individuals from population as guiders and proportionately allocates the individuals following them based on their fitness values and unsuccessful updates. Besides, we design a multi-segment mixed parameter setting to provide suitable parameters for individuals, considering feedback information from the population, individual requirements, and parameters' interaction. Additionally, we devise an adaptive population maintaining mechanism to refresh the population by replacing individuals with higher unsuccessful updates and removing those with poor fitness values. Unlike previous DE versions, our new algorithm dynamically and appropriately explores promising areas, refines parameter suitability, and accelerates convergence by updating and removing unpromising individuals. This enhances the algorithm search efficiency and achieves a good balance between exploration and exploitation. Finally, we evaluate the proposed algorithm's performance by comparing it with 17 typical or state-of-the-art algorithms on IEEE CEC2014 and CEC2017 test suites. Experimental results indicate that the proposed algorithm is a more promising optimizer. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
41. Time and memory scalable algorithms for clustering tendency assessment of big data.
- Author
-
Deshpande, Kartik Vishal and Kumar, Dheeraj
- Subjects
- *
CREDIT card fraud , *BIG data , *DATA structures , *EUCLIDEAN algorithm , *TIME complexity , *ALGORITHMS , *MEMETICS , *DATA security failures - Abstract
Large-volume and high-dimensional big datasets are being generated quickly. They are expected to provide data-driven solutions for various pressing challenges such as cybersecurity, credit card fraud, network intrusion detection, etc. The visual assessment of clustering tendency (VAT) family of algorithms provides an excellent tool for assessing clustering tendency and subsequent clustering of these novel datasets to extract groups and anomalies to better understand the data generation phenomenon. However, VAT and improved VAT (iVAT) algorithms are plagued with O (n 2) time and space complexity as they use Prim's algorithm for building Euclidean minimum spanning tree (EMST) of the data points, leaving it inapplicable to large datasets. This paper develops three novel time- and memory-scalable algorithms for fast computation of VAT EMST by reducing the search space of the following possible EMST edge and using an efficient data structure, the k-d tree. Next, we develop a novel approach to compute iVAT reordered dissimilarity image (RDI) in a novel memory-preserving manner using the EMST edge lengths calculated previously without having to compute memory intensive n × n VAT RDI. We experimented on several synthetic and real-life datasets to showcase that the proposed approaches can help quickly assess their clustering tendency in a memory-saving manner. We also demonstrate the applicability of the proposed methods for the anomaly detection task in large volumes of high-dimensional data. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
42. Multi-task modeling and multifactorial optimization for path coverage problem of automated test case generation.
- Author
-
Wang, Xupeng, Hu, Zhongbo, Shi, Lingyi, Cai, Gaocheng, and Su, Qinghua
- Subjects
OPTIMIZATION algorithms ,ASSORTATIVE mating ,MEMETICS - Abstract
Recent research in automated test case generation (ATCG) focuses on multi-objective optimization using functions based on path structure (F-PS) to solve the path coverage (PC) problem. Despite the similarity among F-PSs, the existing multi-objective optimization models fail to consider using the similarity to effectively promote optimization among multiple objectives. Inspired by the similarity and multitask optimization, this paper first establishes a multitasking path coverage (MtPC) model with two different F-PSs as its tasks. A multifactorial optimization framework for solving MtPC model (MfO-PC) is then proposed to optimize the tasks by assortative mating and to cooperatively generate desired test cases by automatic assignment strategy. Three multifactorial optimization algorithms based on the framework are then designed and tested on twelve benchmark programs. Experimental results show that the effectiveness of the proposed model and the designed algorithms based on MfO-PC framework achieve the highest path coverage with fewer test cases and less running time than some compared state-of-the-art algorithms. • This marks the first application of multitask optimization for automated test case generation in path coverage. • Propose a multitasking path coverage model by two similar functions based on path structure. • Design a multifactorial optimization framework for solving the proposed model. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
43. A many-objective evolutionary algorithm based on reference vector guided selection and two diversity and convergence enhancement strategies.
- Author
-
Yang, Lei, Zhang, Yuanye, Cao, Jiale, Li, Kangshun, and Wang, Dongya
- Subjects
EVOLUTIONARY algorithms ,CULTURAL pluralism ,MEMETICS ,PROBLEM solving ,HOTEL suites - Abstract
Achieving the balance between convergence and diversity is a key and challenging issue in many-objective optimization. Reference vector guided selection is an exemplary method for decomposition-based many-objective evolutionary algorithms (MaOEAs). However, there are some problems with it such as insufficient number of obtained solutions and inefficient convergence evaluation metric. Aiming at solving or alleviating these problems, this paper proposes a many-objective evolutionary algorithm based on reference vector guided selection and two diversity and convergence enhancement strategies. The proposed algorithm introduces two new strategies namely adaptive sparse region detection and convergence-only selection. The former is to adaptively detect sparse regions of current elite population, while the latter is to prevent the elimination of solutions with prominent convergence performance. Together with a newly proposed elite retention strategy, these two strategies can achieve diversity and convergence enhancement on the basis on reference vector guided selection. Besides, A new selection criterion for reference vector guided selection is proposed to better measure the convergence of solutions in high dimensionality. Experimental results on widely used test problem suites up to 15 objectives indicate that the proposed algorithm is highly competitive in comparison with seven state-of-the-art MaOEAs. • Two strategies based on reference vector guided selection for better performance. • An adaptive approach to detect sparse regions for diversity enhancement. • A method to preserve prominent solutions for convergence enhancement. • An elite retention strategy to fill vacancies in elite population. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
44. Teaching-learning-based optimization algorithm with dynamic neighborhood and crossover search mechanism for numerical optimization.
- Author
-
Zeng, Zhibo, Dong, He, Xu, Yunlang, Zhang, Wei, Yu, Hangcheng, and Li, Xiaoping
- Subjects
OPTIMIZATION algorithms ,MEMETICS ,NEIGHBORHOODS - Abstract
This paper presents an improving teaching-learning-based optimization algorithm (called DRCMTLBO) combined with the dynamic ring neighborhood topology. Firstly, based on the individuals' fitness distribution and clustered state within and beyond the ring neighborhood, two evaluations of relative neighborhood quality (RNQ) are developed to separately guide population evolution. On the one hand, a dynamic neighborhood strategy driven by fitness-based evaluation is used to adjust neighborhood radius, maintaining individual variability and neighborhood diversity. On the other hand, to utilize individuals' information of the entire topology, a novel crossover search mechanism driven by Euclidean distance-based evaluation is used to expand the search space, determining whether individuals should enhance exploitation within the neighborhood or exploration beyond the neighborhood. Finally, the above strategies are embedded into the TLBO algorithm, assisted by improved search approaches that achieve a significant balance between exploitation and exploration. Numerical computation results on functions of CEC2014 and CEC2020 show that our proposed DRCMTLBO algorithm outperforms other ten typical algorithms significantly, and its computational performance can compete with several CEC winner algorithms. • Proposing two relative neighborhood quality evaluations for expanding information. • Designing a dynamic neighborhood strategy adapted to the search tendency. • A novel crossover mechanism enhances the exploration of opposite neighborhoods. • Validating the DRCMTLBO algorithm on CEC2014 and CEC2020. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. Reducing the "digital divide" to reap the "digital dividend": spatial differences and convergence of the digital economy in cities of China.
- Author
-
Jing Han, Yang Song, and Jisheng Chen
- Subjects
HIGH technology industries ,ECONOMIC conditions in China ,DIGITAL divide ,SUSTAINABLE development ,REGIONAL differences ,DIVIDENDS ,MEMETICS - Abstract
Bridging the "digital divide" to benefit from the "digital dividend" reflects the inclusive development of the digital economy, which affects the sustainable development of the overall economy and society. Based on the different spatial perspectives of certain regions, city clusters, and cities, this study presents the "digital divide" and "digital dividend" for the development of China's digital economy. A combined entropy method was used to measure the development of the digital economy in cities of China from 2011 to 2019. The Dagum Gini coefficient and its decomposition were used to explore the spatial differences of the digital economy and their sources, while the coefficient of variation and the spatial panel model were used to examine the convergence of the digital economy. Our findings suggest that the development level of the digital economy in Chinese cities has continuously improved. Second, the spatial differences of the digital economy in the four regions and nine city clusters of China that were investigated are decreasing, indicating that the "digital divide" is narrowing and presenting a significant "digital dividend." The total differences were mainly owing to those between the regions (among the city clusters). Third, the development of the digital economy in the four regions and nine city clusters demonstrated characteristics of σ, β, and club convergences. Finally, the evolution path of the spatial differentiation of the digital economy in cities of China is presented, and the "digital dividend" also demonstrates different stage characteristics. This study analyzed the spatial differences in the digital economy of cities in China and clarified the convergence and influence mechanisms of its development at different spatial scales. The findings provide a basis for the evolution of spatial and temporal patterns of the digital economy of cities in China and presents policy implications for promoting the regionally coordinated and sustainable development of the digital economy. Suggestions from the national and regional levels are more necessary to further narrow the "digital divide," create a "digital dividend," and digital drives sustainable economic development. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
46. Song and dance: a memetic angle on the evolution of musicality and music via case studies of a musemeplex in Saint-Saëns and ABBA.
- Author
-
Jan, Steven
- Subjects
SINGING ,BIOLOGICAL evolution ,MUSIC theory ,CULTURAL transmission ,SONGS ,PROSPECT theory ,ANGLES ,SOCIAL evolution - Abstract
Applying the theory of memetics to music offers the prospect of reconciling general Darwinian principles with the style and structure of music. The nature of the units of cultural evolution in music--memes or, more specifically, musemes--can potentially shed light on the evolutionary processes and pressures attendant upon early-hominin musicality. That is, primarily conjunct, narrow-tessitura musemes (those conforming to Ratner's "singing style," and its instrumental assimilations) and primarily disjunct, wide-tessitura musemes (those conforming to Ratner's "brilliant style," and its vocal assimilations) appear to be the outcome of distinct cultural-evolutionary processes. Moreover, musemes in each category arguably acquire their fecundity (perceptual-cognitive salience, and thus transmissibility) by appealing to different music-underpinning brain and body subsystems. Given music's status as an embodied phenomenon, both singing-style and brilliant-style musemes recruit and evoke image schemata, but those in the former category draw primarily upon vocal images of line, direction and continuity; whereas those in the latter category draw primarily upon rhythmic impetus and energy. These two museme-categories may have been molded by distinct biological-evolutionary processes--the evolution of fine vocal control, and that of rhythmic synchronisation, respectively; and they might--via the process of memetic drive--have themselves acted as separate and distinct selection pressures on biological evolution, in order to optimize the environment for their replication. As a case-study of (primarily) singing-style musemes, this article argues that a passage from the love duet "Mon coeur s'ouvre à ta voix" from Camille Saint-Saëns' opera Samson et Dalila op. 47 (1877) is the cultural-evolutionary antecedent of the Introduction/Chorus/Outro material of ABBA's song "The Winner Takes It All." Discussion of their melodic and harmonic similarities supports a memetic link between elements of Saint-Saëns' duet and ABBA's song. These relationships of cultural transmission are argued to have been impelled by the fecundity of the shared musemes, which arises from the image-schematic and embodied effects of the implication-realisation structures (in Narmour's sense) that comprise them; and which is underwritten by the coevolution of musemes with vocal- and rhythmic-production mechanisms, and associated perceptual-cognitive schemata. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
47. Using Respondent-Driven Sampling to measure abortion safety in restrictive contexts: Results from Kaya (Burkina Faso) and Nairobi (Kenya).
- Author
-
Zan, Moussa L., Owolabi, Onikepe, Baguiya, Adama, Ouédraogo, Ramatou, Bangha, Martin, Kim, Caron, and Rossier, Clémentine
- Subjects
QUALITY control ,SAFETY ,ABORTION ,WOMEN'S health ,ABORTIFACIENTS ,MEMETICS - Abstract
BACKGROUND: Due to restrictive laws and limited service provision, globally the majority of induced abortions are unsafe and remain largely undocumented, despite their negative impact on women's health. OBJECTIVE: The purpose of this study is to test Respondent-Driven Sampling - used previously in HIV research - for abortion, and to measure abortion safety characteristics in the small town of Kaya and surrounding villages (Burkina Faso) and in the slums of Nairobi (Kenya). METHODS: A preliminary qualitative study confirmed that women disclose to network members when seeking an abortion. Using RDS, we recruited 481 abortion-seekers from 12 seeds in Kaya (in max. 10 waves) and 551 abortion-seekers from 8 seeds in Nairobi (in max. 5 waves) in 2021. These respondents reported 487 (Kaya) and 595 (Nairobi) abortions in the previous 3 years. RESULTS: Standard diagnostics showed that the safety characteristics of the abortions of the recruited women were independent of those of the seeds, but sample-wide convergence was reached at relatively high sample sizes (around 300 in Kaya and 400 in Nairobi). More advanced checks indicated convergence (or tendency towards convergence) across seeds, except for one indicator in one site. In Kaya, most women used plant-based methods (50%) and unidentified pills (Medical Abortion (MA) in appearance) (33%). In Nairobi, women used unidentified pills (likely MA) (38% of abortions), followed by plant-based (21%) and known harmful methods (19%); pharmacists were frequent providers (45%). In Kaya, abortions occurred in the first trimester (98%), but less so in Nairobi (70%). The level of reported likely infected complications was similar across sites (6.2% in Kaya versus 9.6% in Nairobi). CONCLUSIONS: While the RDS misses non-networked abortion-seekers (an arguably small share of abortion-seekers in the sites), the different available quality checks yielded positive signals as to the possibility of using RDS to obtain data representative of networked abortions. The data revealed contrasting safety characteristics and abortion-seeker profiles across sites. CONTRIBUTION: RDS is cheap, yields large and diverse samples of abortion-seekers, and is equipped with built-in quality tests: it is a promising avenue for collecting data on abortions in restrictive settings where abortion-seekers are highly networked, although a number of limitations remain and further development is needed. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
48. A general convergence analysis method for evolutionary multi-objective optimization algorithm.
- Author
-
Cai, Tie and Wang, Hui
- Subjects
- *
OPTIMIZATION algorithms , *EVOLUTIONARY algorithms , *PRINCIPAL components analysis , *MISSING data (Statistics) , *MEMETICS , *PARETO optimum - Abstract
Convergence analysis of multi-objective optimization algorithm has been an area of vital interest to the research community. With this regard, a number of approaches have been proposed and studied. However, these studies and developed proposals cannot cope with more than 3-dimensional optimization problems. Generally speaking, interpolation planes are formed by 3-dimension data. So, when the dimensionality of the Pareto front is more than 3, the dimensionality of Pareto front will be reduced to 3 by involving principal component analysis. This may lead to some important data being missed. Due to missing data, the formed interpolation plane is usually inaccurate and uneven. This will give rise to difficulties to evaluate the distance between the Pareto front and the optimal Pareto front. Subsequently, it is not easy to evaluate exact convergence time and with this regard the existing solutions lack general. Having this in mind, this paper develops a general convergence analysis (GCAM) for evolutionary multi-objective optimization algorithm (EMOA). In this approach, two originality aspects come to existence: one associates with the interpolation plane convergence analysis while the second concerns the improved drift analysis of evolutionary algorithm. Firstly, for more than 3-dimensional space, the dimensionality of the Pareto front set becomes reduced to 3 through a locally linear embedding. This overcomes the irregular interpolation plane problem and produce a high-quality interpolation. Secondly, this study originally analyzes the convergence of EMOA by engaging an improved drift analysis. Finally, we determine the first stopping time of EMOA by analyzing the convergence metric. The experimental results demonstrate that the proposed method exhibits better performance in comparison with CAD, CAL, and CAC. Specifically, the error proportion of SMS-EMOA, AR-MOEA, SPEA2+SDE, GFM-MOEA has been decreased by 12%, 15%, 21%, 19% and 17%, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
49. A cluster prediction strategy with the induced mutation for dynamic multi-objective optimization.
- Author
-
Xu, Kangyu, Xia, Yizhang, Zou, Juan, Hou, Zhanglu, Yang, Shengxiang, Hu, Yaru, and Liu, Yuan
- Subjects
- *
EVOLUTIONARY algorithms , *FORECASTING , *MEMETICS - Abstract
Dynamic multi-objective optimization problems (DMOPs) are multi-objective optimization problems in which at least one objective and/or related parameter vary over time. The challenge of solving DMOPs is to efficiently and accurately track the true Pareto-optimal set when the environment undergoes changes. However, many existing prediction-based methods overlook the distinct individual movement directions and the available information in the objective space, leading to biased predictions and misleading the subsequent search process. To address this issue, this paper proposes a prediction method called IMDMOEA, which relies on cluster center points and induced mutation. Specifically, employing linear prediction methods based on cluster center points in the decision space enables the algorithm to rapidly capture the population's evolutionary direction and distributional shape. Additionally, to enhance the algorithm's adaptability to significant environmental changes, the induced mutation strategy corrects the population's evolutionary direction by selecting promising individuals for mutation based on the predicted result of the Pareto front in the objective space. These two complementary strategies enable the algorithm to respond faster and more effectively to environmental changes. Finally, the proposed algorithm is evaluated using the JY, dMOP, FDA, and F test suites. The experimental results demonstrate that IMDMOEA competes favorably with other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. Multi-objective compression for CNNs via evolutionary algorithm.
- Author
-
Lian, Youzao, Peng, Peng, Jiang, Kai, and Xu, Weisheng
- Subjects
- *
EVOLUTIONARY algorithms , *CONVOLUTIONAL neural networks , *DATA compression , *DIFFERENTIAL evolution , *MEMETICS - Abstract
Filter pruning has been successfully applied in convolutional neural networks (CNN) to reduce model redundancy and speed up inference. Current pruning algorithms require setting a target value for a pruned model, which is not conducive to the rapid implementation in practical application. Firstly, for a target model, it may be necessary to pay attention to multiple performances at the same time, but it is hard to keep the best trade-off among indicators. Secondly, it is time-consuming to find a suitable model by pruning in a trial-and-error way. Therefore, this paper attempts to combine the most concerning indicators into a search target, rather than producing a model that only contains some pre-defined constraint. Specifically, the multi-object differential evolution algorithm is introduced into the search process, which is used to obtain a set of sparse structures that have taken all useful indicators into account. Then select proper sub-network to retrain from search results according to actual requirements. Experiments on multiple advanced CNN networks show that our algorithm can compress different indicators while keeping comparable or better performance than prior works. On ImageNet, pruning based on ResNet50 reduces FLOPs by 60.4%, while the accuracy of top-1 and top-5 only loses 1.09% and 0.57%, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.