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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

• 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]

• 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]

• 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]