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After emergencies occur, decision-makers can reference historical cases with similar causes to take similar emergency response measures. However, information about emergencies is usually recorded and stored in textual form, and it is difficult for decision-makers to obtain effective information from large amounts of textual data and make decisions that balance various factors. To address these issues, this paper presents an emergency assisted decision-making model based on a knowledge graph and many-objective optimization. First, we preprocess the information by extracting entities to construct a knowledge graph of emergencies to make the event information more structured and easier to use. A knowledge graph is also used to narrow the range of matching historical events. Second, we construct a many-objective model with four objectives: similarity, diversity, processing time, and resource cost. Finally, combining the model characteristics, we design genetic operations for duplicate location matching and substitution removal strategies to obtain the nonrepetitive VaEA algorithm. This algorithm is used to optimize the model and generate a list of reference cases in the reduced matching range to provide rescue strategy suggestions for the current situation. The experimental results show that the algorithm outperforms the comparison algorithms under all four evaluation metrics. This indicates that the method in this paper can match the higher-quality historical emergency solutions applicable to the current decision-making situation in the case base and provide support for decision-makers to respond reasonably in emergencies. [ABSTRACT FROM AUTHOR]

Many results have been obtained when studying scientific papers citations databases in a network perspective. Articles can be ranked according to their current in-degree and their future popularity or citation counts can even be predicted. The dynamical properties of such networks and the observation of the time evolution of their nodes started more recently. This work adopts an evolutionary perspective and proposes an original algorithm for the construction of genealogical trees of scientific papers on the basis of their citation count evolution in time. The fitness of a paper now amounts to its in-degree growing trend and a “dying” paper will suddenly see this trend declining in time. It will give birth and be taken over by some of its most prevalent citing “offspring”. Practically, this might be used to trace the successive published milestones of a research field. [ABSTRACT FROM AUTHOR]

We separate the component spectra of the composite-spectrum binary 14 Serpentis, derive a precise double-lined (SB2) orbit for the system, and investigate the physical properties and evolutionary states of its component stars. The 260-day binary is comprised of an early-K giant plus a late-Am star that is rotating at ∼60-70 km s−1. We find that the system has a mass ratio ( M1/ M2) of 1.14 ± 0.01, and that the cool star is ∼1.7 times as bright as its hot companion in V, though ∼2.5 times brighter bolometrically. We derive individual masses and logarithmic luminosities of the cool star and the hotter one as 2.05 and 1.8 M⊙, and 1.57 and 1.18 log L⊙, respectively, with corresponding uncertainties close to 0.1 M⊙ and 0.07 log L⊙. Fitting evolutionary tracks to the H-R diagram positions of the two stars suggests that the cool primary is part-way up the red-giant branch and that the system has been evolving for more than 109 years; the absence of Li from its spectrum may support the latter statement. The hotter, less massive companion appears to have left the zero-age main sequence, which accords with the log g<4.0 indicated by comparisons with synthetic spectra, though its radius is currently only 2.5 R⊙. [ABSTRACT FROM AUTHOR]

Environmental concerns and soaring energy prices have brought huge pressure of energy saving and emission reduction to tissue paper mills. Electricity is one of the main energy sources of tissue paper mills. The production characteristics of tissue paper mills make it easy to decrease energy cost by using time-of-use (TOU) electricity tariffs. This study investigates the bi-objective energy-efficiency scheduling of tissue paper mills under time-of-use electricity tariffs, the objectives of which are makespan and energy cost. First, considering the processing energy cost, setup energy cost, and transportation energy cost, an energy cost model of a tissue paper mill under TOU electricity tariffs is established. Second, the energy-efficiency scheduling model under TOU electricity tariffs is built based on the energy cost model. Finally, on the basis of decomposition and teaching–learning optimization, this study proposes a novel multi-objective evolutionary algorithm and further combined with the variable neighborhood search to solve the problem. The case study results demonstrate that our study of tissue paper mill energy saving is feasible, and the proposed method has better performance than the existing methods. [ABSTRACT FROM AUTHOR]

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The article reports on receiving of more than 5,000 citations by a research paper authored by Indian scientists as of May 2015. It states that the paper has been published by researchers from Indian Institute of Technology, Kanpur which focuses multiobjective evolutionary algorithms that use nondominated sorting and sharing and mentions that according to database website Web of Science most of the citations have been received from China.

As the power source of the engine, the Fuel Pump(FP) plays a vital role in the safe operation of the aircraft. Due to the complexity of the working mechanism of Aviation Fuel Pumps (AFP) and the strong correlation between the components, the assessment model cannot reflect the health state of the FPs better, while the initial parameters in the assessment model will affect the assessment effect of the model. Therefore, this paper proposes a health status assessment model that can fully integrate monitoring data. To improve the accuracy of the model parameters, the Random Forest algorithm is used to give the feature weights to make up for the limitation of relying on expert knowledge, and the model parameters are optimized by the Covariance Matrix Adaptive Evolutionary Strategy algorithm, which achieves an accurate assessment of the state. Finally, the AFP test bed was built and the AFP was tested. Compared with other methods, the accuracy of the proposed method in this question reaches 96%, which is greatly superior to other methods and verifies the effectiveness of the proposed method. It also provides an outlook on future research directions for health state assessment. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is widely recognized as a highly effective evolutionary algorithm for global optimization. It has proven its efficacy in tackling diverse problems across various fields and real-world applications. DE boasts several advantages, such as ease of implementation, reliability, speed, and adaptability. However, DE does have certain limitations, such as suboptimal solution exploitation and challenging parameter tuning. To address these challenges, this research paper introduces a novel algorithm called Enhanced Binary JADE (EBJADE), which combines differential evolution with multi-population and elites regeneration. The primary innovation of this paper lies in the introduction of strategy with enhanced exploitation capabilities. This strategy is based on utilizing the sorting of three vectors from the current generation to perturb the target vector. By introducing directional differences, guiding the search towards improved solutions. Additionally, this study adopts a multi-population method with a rewarding subpopulation to dynamically adjust the allocation of two different mutation strategies. Finally, the paper incorporates the sampling concept of elite individuals from the Estimation of Distribution Algorithm (EDA) to regenerate new solutions through the selection process in DE. Experimental results, using the CEC2014 benchmark tests, demonstrate the strong competitiveness and superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Artificial intelligence is changing and influencing our world. As one of the main algorithms in the field of artificial intelligence, convolutional neural networks (CNNs) have developed rapidly in recent years. Especially after the emergence of NASNet, CNNs have gradually pushed the idea of AutoML to the public's attention, and large numbers of new structures designed by automatic searches are appearing. These networks are usually based on reinforcement learning and evolutionary learning algorithms. However, sometimes, the blocks of these networks are complex, and there is no small model for simpler tasks. Therefore, this paper proposes POSS-CNN aiming at target recognition and detection, which employs a multi-branch CNN structure with PSNC and a method of automatic parallel selection for super parameters based on a multi-branch CNN structure. Moreover, POSS-CNN can be broken up. By choosing a single branch or the combination of two branches as the "benchmark", as well as the overall POSS-CNN, we can achieve seven models with different precision and operations. The test accuracy of POSS-CNN for a recognition task tested on a CIFAR10 dataset can reach 86.4%, which is equivalent to AlexNet and VggNet, but the operation and parameters of the whole model in this paper are 45.9% and 45.8% of AlexNet, and 29.5% and 29.4% of VggNet. The mAP of POSS-CNN for a detection task tested on the LSVH dataset is 45.8, inferior to the 62.3 of YOLOv3. However, compared with YOLOv3, the operation and parameters of the model in this paper are reduced by 57.4% and 15.6%, respectively. After being accelerated by WRA, POSS-CNN for a detection task tested on an LSVH dataset can achieve 27 fps, and the energy efficiency is 0.42 J/f, which is 5 times and 96.6 times better than GPU 2080Ti in performance and energy efficiency, respectively. [ABSTRACT FROM AUTHOR]

As potent approaches for addressing computationally expensive optimization problems, surrogate-assisted evolutionary algorithms (SAEAs) have garnered increasing attention. Prevailing endeavors in evolutionary computation predominantly concentrate on expensive continuous optimization problems, with a notable scarcity of investigations directed toward expensive combinatorial optimization problems (ECOPs). Nevertheless, numerous ECOPs persist in practical applications. The widespread prevalence of such problems starkly contrasts the limited development of relevant research. Motivated by this disparity, this paper conducts a comprehensive survey on SAEAs tailored to address ECOPs. This survey comprises two primary segments. The first segment synthesizes prevalent global, local, hybrid, and learning search strategies, elucidating their respective strengths and weaknesses. Subsequently, the second segment furnishes an overview of surrogate-based evaluation technologies, delving into three pivotal facets: model selection, construction, and management. The paper also discusses several potential future directions for SAEAs with a focus towards expensive combinatorial optimization. [ABSTRACT FROM AUTHOR]

Manually designed convolutional neural networks have demonstrated excellent performance in various domains, but designing neural networks suitable for specific tasks poses significant challenges, and the emergence of Neural Structure Search (NAS) provides a new solution to this problem. However, existing algorithms either focus solely on network lightweight, resulting in subpar network performance, or excessively emphasize performance, leading to substantial network redundancy. With consideration for both network parameters and performance, this paper designs a hybrid search space based on residual modules and RepVGG modules using genetic algorithm, and stacks them together to form a more efficient network. To achieve this, we propose an efficient variable-length encoding strategy, utilizing units as the fundamental encoding space to encode variable-length individuals; we design evolutionary operations encompassing single-point crossover and three types of mutation operators to ensure population diversity; during training, a random forest-based performance predictor is employed to significantly shorten the network search time. To demonstrate the effectiveness of the proposed algorithm, we introduce the concept of transfer learning, which involves decoding the globally optimal solution, fine-tuning it, and then transferring it to three categories of real-world application datasets. Through comparisons with various algorithms, our approach consistently achieved leading performance. [ABSTRACT FROM AUTHOR]

This review comprehensively examines the burgeoning field of intelligent techniques to enhance power systems' stability, control, and protection. As global energy demands increase and renewable energy sources become more integrated, maintaining the stability and reliability of both conventional power systems and smart grids is crucial. Traditional methods are increasingly insufficient for handling today's power grids' complex, dynamic nature. This paper discusses the adoption of advanced intelligence methods, including artificial intelligence (AI), deep learning (DL), machine learning (ML), metaheuristic optimization algorithms, and other AI techniques such as fuzzy logic, reinforcement learning, and model predictive control to address these challenges. It underscores the critical importance of power system stability and the new challenges of integrating diverse energy sources. The paper reviews various intelligent methods used in power system analysis, emphasizing their roles in predictive maintenance, fault detection, real-time control, and monitoring. It details extensive research on the capabilities of AI and ML algorithms to enhance the precision and efficiency of protection systems, showing their effectiveness in accurately identifying and resolving faults. Additionally, it explores the potential of fuzzy logic in decision-making under uncertainty, reinforcement learning for dynamic stability control, and the integration of IoT and big data analytics for real-time system monitoring and optimization. Case studies from the literature are presented, offering valuable insights into practical applications. The review concludes by identifying current limitations and suggesting areas for future research, highlighting the need for more robust, flexible, and scalable intelligent systems in the power sector. This paper is a valuable resource for researchers, engineers, and policymakers, providing a detailed understanding of the current and future potential of intelligent techniques in power system stability, control, and protection. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of accurate and interpretable prediction of energy consumption in residential buildings. The solution that we propose in this work employs the knowledge discovery machine learning approach combining fuzzy systems with evolutionary optimization. The contribution of this work is twofold, including both methodology and experimental investigations. As far as methodological contribution is concerned, in this paper, we present an original designing procedure of fuzzy rule-based prediction systems (FRBPSs) for accurate and transparent energy consumption prediction in residential buildings. The proposed FRBPSs are characterized by a genetically optimized accuracy–interpretability trade-off. The trade-off optimization is carried out by means of multi-objective evolutionary optimization algorithms—in particular, by our generalization of the well-known strength Pareto evolutionary algorithm 2 (SPEA2). The proposed FRBPSs' designing procedure is our original extension and generalization (for regression problems operating on continuous outputs) of an approach to designing fuzzy rule-based classifiers (FRBCs) we developed earlier and published in 2020 in this journal. FRBCs operate on discrete outputs, i.e., class labels. The experimental contribution of this work includes designing the collection of FRBPSs for residential building energy consumption prediction using the data set published in 2024 and available from Kaggle Database Repository. Moreover, the comparison with 20 available alternative approaches is carried out, demonstrating that our approach significantly outperforms alternative methods in terms of interpretability and transparency of the energy consumption predictions made while remaining comparable or slightly superior in terms of the accuracy of those predictions. [ABSTRACT FROM AUTHOR]

Evolutionary multi-objective clustering (EMOC) algorithms have gained popularity recently, as they can obtain a set of clustering solutions in a single run by optimizing multiple objectives. Particularly, in one type of EMOC algorithm, the number of clusters k is taken as one of the multiple objectives to obtain a set of clustering solutions with different k. However, the numbers of clusters k and other objectives are not always in conflict, so it is impossible to obtain the clustering solutions with all different k in a single run. Therefore, evolutionary multi-objective k-clustering (EMO-KC) has recently been proposed to ensure this conflict. However, EMO-KC could not obtain good clustering accuracy on high-dimensional datasets. Moreover, EMO-KC's validity is not ensured as one of its objectives (SSDexp, which is transformed from the sum of squared distances (SSD)) could not be effectively optimized and it could not avoid invalid solutions in its initialization. In this paper, an improved evolutionary multi-objective clustering algorithm based on autoencoder (AE-IEMOKC) is proposed to improve the accuracy and ensure the validity of EMO-KC. The proposed AE-IEMOKC is established by combining an autoencoder with an improved version of EMO-KC (IEMO-KC) for better accuracy, where IEMO-KC is improved based on EMO-KC by proposing a scaling factor to help effectively optimize the objective of SSDexp and introducing a valid initialization to avoid the invalid solutions. Experimental results on several datasets demonstrate the accuracy and validity of AE-IEMOKC. The results of this paper may provide some useful information for other EMOC algorithms to improve accuracy and convergence. [ABSTRACT FROM AUTHOR]

Background: For non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem. Methods: The multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space. Results: The proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels. Conclusion: The formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) are a type of mobile ad hoc network that forms a unified wireless communication network between vehicles and roadside nodes. Roadside units (RSUs), as the infrastructure and key component of VANETs, play a critical role in improving the performance of VANETs. Their deployment can effectively improve the communication performance of the network. The goal of the RSU deployment (RSUD) problem is to install as few RSUs as possible on both sides of roads or intersections so that they can cover most areas and achieve better communication performance. This paper proposes a multi-objective optimization model to solve the RSUD problem using the characteristics of RSUDs on urban roads. Our optimized target includes three performance indicators, which are the deployment cost of RSUs, their coverage area, and communications. Since these three indicators cannot achieve consensus, this paper proposes a multi-objective evolutionary algorithm, the NSGA-II Pareto optimal solution, to solve the RSUD problem. The simulation results show the effectiveness of the multi-objective model and method. Finally, we present an experiment featuring RSU deployment that is based on a real traffic environment and uses OpenStreetMap; this experiment proves that our proposed method is practical. [ABSTRACT FROM AUTHOR]

The development of offshore oil fields confronts challenges associated with high water cut and low displacement efficiency. Reservoir injection-production optimization stands out as an effective means to reduce costs and enhance efficiency in offshore oilfield development. The process of optimizing injection and production in offshore oil reservoirs involves designing strategies for a large number of wells and optimization time steps, constituting a large-scale, complex, and costly optimization computation problem. In recent years, with the rapid advancements in big data and artificial intelligence technologies, sophisticated evolutionary computation methods have found widespread application in reservoir injection-production optimization problems. However, the abundance of intelligent optimization algorithms raises the question of how to choose a method suitable for the complex optimization background of offshore oilfield injection-production optimization. This paper provides a detailed overview of the application of an existing differential evolution algorithm (DE), conventional surrogate-assisted evolutionary algorithm (CSAEA), and global–local surrogate-assisted differential evolution (GLSADE) in the context of practical offshore oilfield injection-production optimization problems. A comprehensive comparison of their performance differences is presented. The study concludes that the global–local surrogate-assisted evolutionary algorithm is the most suitable method for addressing the current challenges in offshore oilfield injection-production optimization. [ABSTRACT FROM AUTHOR]

Background: Channel selection has become the pivotal issue a ecting the widespread application of non-invasive brain-computer interface systems in the real world. However, constructing suitable multi-objective problem models alongside e ective search strategies stands out as a critical factor that impacts the performance of multi-objective channel selection algorithms. This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems. Methods: In TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on di erent multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA. Results: The performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the e ectiveness of TS-MOEA. Conclusion: The proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can e ectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency. [ABSTRACT FROM AUTHOR]

Autonomous region protection is a significant research area in multi-agent systems, aiming to empower defenders in preventing intruders from accessing specific regions. This paper presents a Multi-agent Region Protection Environment (MRPE) featuring fewer defenders, defender damages, and intruder evasion strategies targeting defenders. MRPE poses challenges for traditional protection methods due to its high nonstationarity and limited interception time window. To surmount these hurdles, we modify evolutionary reinforcement learning, giving rise to the corresponding multi-agent region protection method (MRPM). MRPM amalgamates the merits of evolutionary algorithms and deep reinforcement learning, specifically leveraging Differential Evolution (DE) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). DE facilitates diverse sample exploration and overcomes sparse rewards, while MADDPG trains defenders and expedites the DE convergence process. Additionally, an elite selection strategy tailored for multi-agent systems is devised to enhance defender collaboration. The paper also presents ingenious designs for the fitness and reward functions to effectively drive policy optimizations. Finally, extensive numerical simulations are conducted to validate the effectiveness of MRPM. [ABSTRACT FROM AUTHOR]

In the field of expensive optimization, numerous papers have proposed surrogate-assisted evolutionary algorithms (SAEAs) for a few thousand or even hundreds of function evaluations. However, in reality, low-cost simulations suffice for a lot of real-world problems, in which the number of function evaluations is moderately restricted, e.g., to several thousands. In such moderately restricted scenario, SAEAs become unnecessarily time-consuming and tend to struggle with premature convergence. In addition, tuning the SAEA parameters becomes impractical under the restricted budgets of function evaluations—in some cases, inadequate configuration may degrade performance instead. In this context, this paper presents a fast and auto-tunable evolutionary algorithm for solving moderately restricted expensive optimization problems. The presented algorithm is a variant of adaptive differential evolution (DE) algorithms, and is called emulation-based adaptive DE or EBADE. The primary aim of EBADE is to emulate the principle of sample-efficient optimization, such as that in SAEAs, by adaptively tuning the DE parameter configurations. Specifically, similar to Expected Improvement-based sampling, EBADE identifies parameter configurations that may produce expected-to-improve solutions, without using function evaluations. Further, EBADE incepts a multi-population mechanism and assigns a parameter configuration to each subpopulation to estimate the effectiveness of parameter configurations with multiple samples carefully. This subpopulation-based adaptation can help improve the selection accuracy of promising parameter configurations, even when using an expected-to-improve indicator with high uncertainty, by validating with respect to multiple samples. The experimental results demonstrate that EBADE outperforms modern adaptive DEs and is highly competitive compared to SAEAs with a much shorter runtime. [ABSTRACT FROM AUTHOR]

The advancement toward sustainable infrastructure presents complex multi-objective optimization (MOO) challenges. This paper expands the current understanding of design frameworks that balance cost, environmental impacts, social factors, and structural integrity. Integrating MOO with multi-criteria decision-making (MCDM), the study targets enhancements in life cycle sustainability for complex engineering projects using precast modular road frames. Three advanced evolutionary algorithms—NSGA-II, NSGA-III, and RVEA—are optimized and deployed to address sustainability objectives under performance constraints. The efficacy of these algorithms is gauged through a comparative analysis, and a robust MCDM approach is applied to nine non-dominated solutions, employing SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR decision-making techniques. An entropy theory-based method ensures systematic, unbiased criteria weighting, augmenting the framework's capacity to pinpoint designs balancing life cycle sustainability. The results reveal that NSGA-III is the algorithm converging towards the most cost-effective solutions, surpassing NSGA-II and RVEA by 21.11% and 10.07%, respectively, while maintaining balanced environmental and social impacts. The RVEA achieves up to 15.94% greater environmental efficiency than its counterparts. The analysis of non-dominated solutions identifies the A 4 design, utilizing 35 MPa concrete and B500S steel, as the most sustainable alternative across 80% of decision-making algorithms. The ranking correlation coefficients above 0.94 demonstrate consistency among decision-making techniques, underscoring the robustness of the integrated MOO and MCDM framework. The results in this paper expand the understanding of the applicability of novel techniques for enhancing engineering practices and advocate for a comprehensive strategy that employs advanced MOO algorithms and MCDM to enhance sustainable infrastructure development. [ABSTRACT FROM AUTHOR]

In the trajectory planning of crewless ships, the artificial potential field method is commonly used. The results obtained using the classic potential field model for path design are not optimal and cannot fully meet the trajectory design requirements of uncrewed ships. This paper uses the evolutionary potential field model for trajectory planning. The evaluation formula of the potential path is combined with the differential evolution algorithm to evaluate and optimize the potential. A quadratic optimization smoothing algorithm is designed to limit the maximum turning angle of the uncrewed ship. Simulation experiments show that this method is effective and reliable. [ABSTRACT FROM AUTHOR]

When constructing an investment portfolio, it is important to maximize returns while minimizing risks. This portfolio optimization can be considered as a multi-objective optimization problem that is solved by means of multi-objective evolutionary algorithms. The use of multi-objective evolutionary algorithms (MOEAs) provides an effective approach for dealing with the complex data involved in multi-objective optimization problems. However, current MOEAs often rely on a single strategy to obtain optimal solutions, leading to premature convergence and an insufficient population diversity. In this paper, a new MOEA called the Synergistic MOEA with Diffusion Population Generation (DPG-SMOEA) is proposed to address these limitations by integrating MOEAs with diffusion models. To train the diffusion model, a mixed memory pool strategy is optimized, which collects improved solutions from the MOEA/D-AEE, an optimized MOEA, as training samples. The trained model is then used to generate offspring. Considering the cold-start mechanism of the diffusion model, particularly during the training phase where it is not suitable for generating initial offspring, this paper adjusts and optimizes the collaborative strategy to enhance the synergy between the diffusion model and MOEA/D-AEE. Experimental validation of the DPG-SMOEA demonstrates the advantages of using diffusion models in low-dimensional and relatively continuous data analysis. The results show that the DPG-SMOEA performs well on the low-dimensional Hang Seng Index test dataset, while achieving average performance on other high-dimensional datasets, consistent with theoretical predictions. Overall, the DPG-SMOEA achieves better results compared to MOEA/D-AEE and other multi-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

Unfair competition on internet platforms (UCIP) has become a critical issue restricting the platform economy's healthy development. This paper applies evolutionary game theory to study how to utilize multiple subjects' synergy to supervise UCIP effectively. First, the "multi-agent co-governance" mode of UCIP is constructed based on the traditional "unitary supervision" mode. Second, the government and internet platform evolutionary game models are built under two supervision modes. Finally, MATLAB is used to simulate and analyze the evolutionary stage and parameter sensitivity. In addition, we match the model's evolutionary stage with China's supervisory process. The results show that (1) the Chinese government's supervision of UCIP is in the transitional stage from "campaign-style" to "normalization." (2) Moderate government supervision intensity is essential to guide the game system to evolve toward the ideal state. If the supervision intensity is too high, it will inhibit the enthusiasm for supervision. If the supervision intensity is too low, it cannot form an effective deterrent to the internet platforms. (3) When the participation of industry associations and platform users is low, it can only slow down the evolutionary speed of the game system's convergence to the unfavorable state. Nevertheless, it cannot reverse the evolutionary result. (4) Maintaining the participation level of industry associations and platform users above a specific threshold value while increasing punishment intensity will promote the transition of government supervision from the "campaign-style" to the "normalization" stage. This paper provides ideas and references for the Chinese government to design a supervision mechanism for UCIP. [ABSTRACT FROM AUTHOR]

To address the challenges posed by the fast-charging demand of electric vehicles, causing feeder load and voltage imbalances during operation, this paper introduces a spatio-temporal pricing strategy tailored to enhance feeder operation equilibrium. This approach facilitates the spatio-temporal guidance of fast-charging loads for electric vehicles in operation. This paper begins by formulating a spatio-temporal distribution model for electric vehicle fast-charging loads, considering owners' preferences. It further develops a behavioral model for the travel choices of electric vehicles, illustrating the impact of spatio-temporal electricity pricing at fast-charging stations on load distribution. Next, it proposes a multi-objective spatio-temporal pricing model and its solution method specifically designed for feeder-balance-oriented fast-charging stations. This model targets the minimization of the spatio-temporal imbalance in feeder voltage and load. It takes a comprehensive approach, considering the constraints of the spatio-temporal load distribution model and optimal power flow model. The resulting spatio-temporal pricing model for fast-charging stations is effectively solved using the extended Pareto evolutionary algorithm. To validate the effectiveness of the proposed method in achieving feeder balancing, this paper analyzes two examples: a self-built 29- node road network and a 9-node distribution network, as well as a 66-node road network and a 33-node distribution network in the Xinjiang region. The results show that the proposed method can effectively guide the charging of electric vehicles and make the load distribution more balanced. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of a two-stage multi-criteria decision-making procedure for order scheduling problems. These problems are represented by a novel nonlinear mixed integer program. Hybridizations of three Multi-Objective Evolutionary Algorithms (MOEAs) based on dominance relations are studied and compared to solve small, medium, and large instances of the joint order batching and picking problem in storage systems with multiple blocks of two and three dimensions. The performance of these methods is compared using a set of well-known metrics and running an extensive battery of simulations based on a methodology widely used in the literature. The main contributions of this paper are (1) the hybridization of MOEAs to deal efficiently with the combination of orders in one or several picking tours, scheduling them for each picker, and (2) a multi-criteria approach to scheduling multiple picking teams for each wave of orders. Based on the experimental results obtained, it can be stated that, in environments with a large number of different items and orders with high variability in volume, the proposed approach can significantly reduce operating costs while allowing the decision-maker to anticipate the positioning of orders in the dispatch area. [ABSTRACT FROM AUTHOR]

Technical indicators use graphic representations of datasets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors, such as the market in which they operate, the size of the time window, and so on. This paper focuses on the real-time optimization of the parameters applied for analyzing time series of data. In particular, we optimize the parameters of some technical financial indicators. We propose the combination of several Multiobjective Evolutionary Algorithms. Unlike other approaches, this paper applies a set of different Multiobjective Evolutionary Algorithms, collaborating to construct a global Pareto Set of solutions. Solutions for financial problems seek high returns with minimal risk. The optimization process is continuous and occurs at the same frequency as the investment time interval. This technique permits the application of the non-dominated solutions obtained with different MOEAs at the same time. Experimental results show that Collaborative Multiobjective Evolutionary Algorithms obtain up to 22% of profit and increase the returns of the commonly used Buy and Hold strategy and other multi-objective strategies, even for daily operations. [ABSTRACT FROM AUTHOR]

The actual situation of carbon-emission reduction in China's power sector has not yet achieved the expected benefits. The rent-seeking behavior of participants in power construction projects (PCPs) hinders the realization of low-carbon benefits. It is necessary to explore the behavioral strategies of the various participants in the low-carbon transition of PCPs. This paper creatively constructs an evolutionary game model of PCPs' participants from the perspective of MRV (monitoring, reporting, and verification) and introduces the influence of the public to provide a comprehensive analysis of strategic equilibrium points. Through numerical simulations with MATLAB R2021a software, this paper explores the strategic choices of participants in different situations and gives relevant inferences and proofs. The results show that the grid company dominates at the initial stage and promotes participants to regulate behaviors. Under the premise of satisfying the system-stability requirements, setting the growth rate of the grid company's punishments to 100% can enhance the willingness for strict supervision, while the growth rate of the supervision costs to 200% significantly decreases the probability of strict supervision. With the integration of MRV and PCPs, participants spontaneously fulfill the carbon-emission-reduction tasks. Reasonable control of input costs can effectively avoid the occurrence of rent-seeking behavior. In addition, this paper sets the public-influence growth rate at 200% and finds that the public plays a greater role in driving participants to fulfill responsibilities. Based on the results, a low-carbon transition mechanism for PCPs under the MRV system is proposed by considering several dimensions, which provides suggestions for participants to fulfill carbon-reduction responsibilities. [ABSTRACT FROM AUTHOR]

In this paper, a novel simulation-based evolutionary method is presented for designing parameter-free MEMS structures with maximum degrees of freedom. This novel design method enabled semiautomatic structure evolution by weighing the attributes of each segment of the structure and yielded an optimal design after multiple iterations. The proposed method was utilized to optimize the pressure-sensitive diaphragm of a piezoresistive pressure sensor (PPS). Finite element method (FEM) simulations revealed that, in comparison to conventional diaphragms without islands and with square islands, the optimized diaphragm increased the stress by 10% and 16% and reduced the nonlinearity by 57% and 77%, respectively. These improvements demonstrate the value of this method. Characterization of the fabricated PPS revealed a high sensitivity of 8.8 mV V−1 MPa−1 and a low nonlinearity of 0.058% FS at 20 °C, indicating excellent sensor performance. [ABSTRACT FROM AUTHOR]

As a new abstract computational model in evolutionary transfer optimization (ETO), single-objective to multi-objective optimization (SMO) is conducted at the macroscopic level rather than the intermediate level for specific algorithms or the microscopic level for specific operators; this method aims to develop systems with a profound grasp of evolutionary dynamic and learning mechanism similar to human intelligence via a "decomposition" style (in the abstract of the well-known "Transformer" article "Attention is All You Need", they use "attention" instead). To the best of our knowledge, it is the first work of SMO for discrete cases because we extend our conference paper and inherit its originality status. In this paper, by implementing the abstract SMO in specialized memetic algorithms, key knowledge from single-objective problems/tasks to the multi-objective core problem/task can be transferred or "gathered" for permutation flow shop scheduling problems, which will reduce the notorious complexity in combinatorial spaces for multi-objective settings in a straight method; this is because single-objective tasks are easier to complete than their multi-objective versions. Extensive experimental studies and theoretical results on benchmarks (1) emphasize our decomposition root in mathematical programming, such as Lagrangian relaxation and column generation; (2) provide two "where to go" strategies for both SMO and ETO; and (3) contribute to the mission of building safe and beneficial artificial general intelligence for manufacturing via evolutionary computation. [ABSTRACT FROM AUTHOR]

In recent years, China's foreign trade has continued to develop and achieved remarkable results, and the economic development trend has been steady and positive overall. However, although there are new opportunities for the development of China's foreign trade, it also faces new challenges and dilemmas. Therefore, in the current international trade situation, it is of great significance to study the impact of China's foreign trade on industrial structure and find the key points for the development of endogenous economic dynamics on this basis. China's industrial structure has undergone a clear process of upgrading and is a major trading country for industrially manufactured goods. The results of the analysis of the subsectors show that the trend of structural upgrading within the industry is equally significant. At the same time, there is often a significant trend of synergistic changes in the percentage of processing trade and the percentage change of trade in high-tech products. Based on this, this paper analyzes the correlation between China's foreign trade structure and industrial structure based on the correlation of mutual influence. The evolutionary algorithm has the advantages of a strong ability to search for global optimal solutions and good robustness, which is applied as the core algorithm of this paper. The study in this paper examines the interaction between industrial structure and trade structure from the perspective of matching the association between industrial structure and trade structure on the basis of measuring the industrial structure and trade structure at the regional level. The experimental results demonstrate the validity of the model in this paper. [ABSTRACT FROM AUTHOR]

In recent years, nanogenerator technology has developed rapidly with the rise of cloud computing, artificial intelligence, and other fields. Therefore, the quick identification of the evolutionary path of nanogenerator technology from a large amount of data attracts much attention. It is of great significance in grasping technical trends and analyzing technical areas of interest. However, there are some limitations in previous studies. On the one hand, previous research on technological evolution has generally utilized bibliometrics, patent analysis, and citations between patents and papers, ignoring the rich semantic information contained therein; on the other hand, its evolution analysis perspective is single, and it is difficult to obtain accurate results. Therefore, this paper proposes a new framework based on the methods of Sentence-BERT and phrase mining, using multi-source data, such as papers and patents, to unveil the evolutionary path of nanogenerator technology. Firstly, using text vectorization, clustering algorithms, and the phrase mining method, current technical themes of significant interest to researchers can be obtained. Next, this paper correlates the multi-source fusion themes through semantic similarity calculation and demonstrates the multi-dimensional technology evolutionary path by using the "theme river map". Finally, this paper presents an evolution analysis from the perspective of frontier research and technology research, so as to discover the development focus of nanogenerators and predict the future application prospects of nanogenerator technology. [ABSTRACT FROM AUTHOR]

The paper considers an innovative model of autonomous charging stations where a program implementing a scheduling algorithm and a set of jobs being scheduled are driven by the same common power source. It is assumed that one of the well-known local search metaheuristics—an evolutionary algorithm—is used for the scheduling process. The algorithm is designed to search for a sequence of charging jobs resulting in a schedule of the minimum length. Since processors with variable processing speeds can be used for computations, this has interesting consequences both from a theoretical and practical point of view. It is shown in the paper that the problem of choosing the right processor speed under given constraints and an assumed scheduling criterion is a non-trivial one. We formulate a general problem of determining the computation speed of the evolutionary algorithm based on the proposed model of a computational task and the adopted problem of scheduling charging jobs. The novelty of the paper consists of two aspects: (i) proposing the new model of the autonomous charging station operating according to the basics of edge computing; and (ii) developing the methodology for dynamically changing the computational speed, taking into account power and energy constraints as well as the results of computations obtained in the current iteration of the algorithm. Some approaches for selecting the appropriate speed of computations are proposed and discussed. Conclusions and possible directions for future research are also given. [ABSTRACT FROM AUTHOR]

Coastal grounding electrodes are currently an important means to alleviate land grounding electrode land constraints. In order to better invert the terrestrial geodesic resistivity in the coastal region, this paper proposes a complete set of inversion technology schemes. First, this paper proposes a layered land model for the coastal region, and a composite geodetic model is modeled by the fold junction of the land model and the ocean. Based on this, an adaptive subdivision boundary element method is proposed for solving the composite soil grounding calculation problem, and the accuracy and advantages of the method are demonstrated by examples. Finally, the paper uses the differential evolutionary algorithm to invert the exploration data of the four-point method in the coastal area, and obtains the parameters of the terrestrial layered geodetic model that meet the engineering requirements. The comparison with the grounding software CDEGS illustrates the effectiveness of the method. This paper carries out the research on the modeling and inversion methods of composite layered soil model, combining advanced numerical calculation methods and artificial intelligence algorithms to provide the support of computational tools for coastal resistivity inversion. [ABSTRACT FROM AUTHOR]

Flood control operation of cascade reservoirs is an important technology to reduce flood disasters and increase economic benefits. Flood forecast information can help reservoir managers make better use of flood resources and reduce flood risks. In this paper, a hierarchical pre-release flood operation rule considering the flood forecast and its uncertainty information is proposed for real-time flood control. A many-objective optimization model considering the cascade reservoir's power generation objective, flood control objective, and navigation objective is established. Then, a region search evolutionary algorithm is applied to optimize the many-objective optimization model in a real-world case study upstream of the Yangtze River basin. The optimization experimental results show that the region search evolutionary algorithm can balance convergence and diversity well, and the HV value is 40% higher than the MOEA/D algorithm. The simulation flood control results of cascade reservoirs upstream of the Yangtze River demonstrate that the optimized flood control rule can increase the average multi-year power generation of cascade reservoirs by a maximum of 27.72 × 108 kWh under the condition of flood control safety. The rules proposed in this paper utilize flood resources by identifying runoff forecast information, and pre-release to the flood limit level 145 m before the big flood occurs, so as to ensure the safety downstream and the dam's own flood control and provide reliable decision support for reservoir managers. [ABSTRACT FROM AUTHOR]

The dismantling and protection of networks is a significant problem that has wide-ranging applications and attracts many researchers. Most current studies only focus on single-layer or one-to-one interdependent networks. However, this paper considers the more realistic case where the links between layers in interdependent networks are one-to-many, and the networks' robustness is studied accordingly. To solve the problem of dissolving interdependent networks under the premise of heterogeneous costs, we propose a cost-constrained elite quadratic evolutionary algorithm (CCEEA) based on cost constraints. Based on the network's prior information, the initial optimal feasible solutions derived from four classical algorithms are regarded as the initial elite individuals of CCEEA. The set of attack nodes is then continuously updated interactively according to a new evolutionary mechanism with flexible updates so that the combination of nodes in the final set of attack nodes can maximally facilitate the disintegration of the network. We conducted experiments on a series of representative networks and showed that on synthetic networks, the CCEEA algorithm outperforms the other four state-of-the-art attack strategies by more than 13% in terms of disintegration, which is up to 25% higher. In particular, it can be up to more than 90% higher in real networks. [ABSTRACT FROM AUTHOR]

This paper investigates the application of genetic programming (GP) for dynamic symbolic regression (SR), addressing the challenge of adapting machine learning models to evolving data in practical applications. Benchmark instances with changing underlying functions over time are defined to assess the performance of a genetic algorithm (GA) as a traditional evolutionary algorithm and an age-layered population structure (ALPS) as an open-ended evolutionary algorithm for dynamic symbolic regression. This study analyzes population dynamics by examining variable frequencies and impact changes over time in response to dynamic shifts in the training data. The results demonstrate the effectiveness of both the GA and ALPS in handling changing data, showcasing their ability to recover and evolve improved solutions after an initial drop in population quality following data changes. Population dynamics reveal that variable impacts respond rapidly to data changes, while variable frequencies shift gradually across generations, aligning with the indirect measure of fitness represented by variable impacts. Notably, the GA shows a strong dependence on mutation to avoid variables becoming permanently extinct, contrasting with the ALPS's unexpected insensitivity to mutation rates owing to its reseeding mechanism for effective variable reintroduction. [ABSTRACT FROM AUTHOR]

This paper deals with an industrial problem of machining line design, which consists in partitioning a given set of operations into several subsets corresponding to workstations and sequencing the operations to satisfy the technical requirements and achieve the best performance of the line. The problem has a complex set of constraints that include partial order on operations, part positioning, inclusion, exclusion, cycle time, and installation of parallel machines on a workstation. The problem is NP-hard and even finding a feasible solution can be a difficult task from the practical point of view. A parallel evolutionary algorithm (EA) is proposed and implemented for execution on a Graphics Processing Unit (GPU). The parallelization in the EA is done by working on several parents in one iteration and in multiple application of mutation operator to the same parent to produce the best offspring. The proposed approach is evaluated on large scale instances and demonstrated superior performance compared to the algorithms from the literature in terms of running time and ability to obtain feasible solutions. It is shown that in comparison to the traditional populational EA scheme the newly proposed algorithm is more suitable for advanced GPUs with a large number of cores. [ABSTRACT FROM AUTHOR]

Formulations of structural optimization problems are proposed in this paper to automatically find the best grouping of columns in 3D steel buildings. In these formulations, the conflicting objective functions, minimized simultaneously, are the weight of the structure and the number of different groups of columns. In other words, the smaller the number of different groups of columns, the greater the weight of the structure, and the greater the number of groups, the smaller the structure's weight. The design variables are the bracing system configuration, column cross-section orientation, and assigned W-shaped profile indices for columns, beams, and braces. The design constraints are the allowable displacements, strength, and geometric considerations. After solving the multi-objective optimization problem, the result is a Pareto front, presenting non-dominated solutions. Three evolutionary algorithms based on differential evolution are adopted in this paper to solve three computational experiments. Even if preliminary groupings of columns are adopted, considering architectural aspects such as the symmetry of the structure, it is possible to discover other interesting structural configurations that will be available to the decision maker, who will be able to make their choices based on the impacts on manufacturing, cutting, transporting, checking and welding. [ABSTRACT FROM AUTHOR]

Uniform equal thickness layering is widely used in 3D printing, which cannot take into account the printing quality and printing efficiency. In this paper, a new adaptive layering algorithm based on multi-objective optimization is proposed for this problem. The algorithm comprehensively considers the surface features of the model, the slope and curvature of the contour, and establishes a multi-objective optimization model with print quality, print time, and feature constraints. And the Pareto optimal solution set of multi-objective optimization is solved by the improved non-dominated sorting genetic algorithm-II (NSGA-II), and the Pareto optimal solution that meets different printing requirements is selected by the Fuzzy-based weighted membership ranking method. Through comparative experiments, the method proposed in this paper reduces the volume error rate by 40.9% and the printing time by 33.3% compared with uniform layering, which can effectively improve the printing quality and printing efficiency. In addition, compared with the existing adaptive layering algorithms, it is also an algorithm with good comprehensive performance. [ABSTRACT FROM AUTHOR]

This paper presents a new version of Multi Agent Collaborative Search (MACS) with Adaptive Weights (named MACS‐AW). MACS is a multi‐agent memetic scheme for multi‐objective optimization originally developed to mix local and population‐based search. MACS was proven to perform well on a number of test cases but had three limitations: (i) the amount of computational resources allocated to each agent was not proportional to the difficulty of the sub‐problem the agent had to solve; (ii) the population‐based search (called social actions in the following) was using only one differential evolution (DE) operator with fixed parameters; (iii) the descent directions were not adapted during convergence, leading to a loss of diversity. In this paper, we propose an improved version of MACS, that implements: (i) a new utility function to better manage computational resources; (ii) new social actions with multiple adaptive DE operators; (iii) an automatic adaptation of the descent directions with an innovative trigger to initiate adaptation. First, MACS‐AW is compared against some state‐of‐art algorithms and its predecessor MACS2.1 on some standard benchmarks. Then, MACS‐AW is applied to the solution of two real‐life optimization problems and compared against MACS2.1. It will be shown that MACS‐AW produces competitive results on most test cases analysed in this paper. On the standard benchmark test set, MACS‐AW outperforms all other algorithms in 11 out of 30 cases and comes second in other 8 cases. On the two real engineering test set, MACS‐AW and its predecessor obtain same results. [ABSTRACT FROM AUTHOR]

Differential evolution algorithm is a very powerful and recently proposed evolutionary algorithm. Generally, only a mutation operator and predefined parameter values of differential evolution algorithm are utilized to solve various optimization problems, which limits the performance of the algorithm. In this paper, six commonly used mutation operators are divided into three categories according to their own features. A mutation pool is established based on the three categories. A parameter pool with three predefined values is designed. During evolution, three mutation operators are randomly chosen from the three categories, and three parameter values are also randomly selected from the parameter pool. The three groups of mutation operators and parameter values are employed to produce trial vectors. The proposed algorithm makes good use of different mutation operators. Three recently proposed differential evolution variants and three non-differential evolution algorithms are used to make comparisons on the 29 testing functions from CEC. The experimental results have demonstrated that the proposed algorithm is very competitive. The proposed algorithm is utilized to solve three real applications, and the results are superior. [ABSTRACT FROM AUTHOR]

Constrained multiobjective optimization problems (CMOPs) are widespread in reality. The presence of constraints complicates the feasible region of the original problem and increases the difficulty of problem solving. There are not only feasible regions, but also large areas of infeasible regions in the objective space of CMOPs. Inspired by this, this paper proposes a bidirectional coevolution method with reverse search (BCRS) combined with a two-stage approach. In the first stage of evolution, constraints are ignored and the population is pushed toward promising regions. In the second stage, evolution is divided into two parts, i.e., the main population evolves toward the constrained Pareto front (CPF) within the feasible region, while the reverse population approaches the CPF from the infeasible region. Then a solution exchange strategy similar to weak cooperation is used between the two populations. The experimental results on benchmark functions and real-world problems show that the proposed algorithm exhibits superior or at least competitive performance compared to other state-of-the-art algorithms. It demonstrates BCRS is an effective algorithm for addressing CMOPs. [ABSTRACT FROM AUTHOR]

Without a well-defined energy management plan, achieving meaningful improvements in human lifestyle becomes challenging. Adequate energy resources are essential for development, but they are both limited and costly. In the literature, several solutions have been proposed for energy management but they either minimize energy consumption or improve the occupant's comfort index. The energy management problem is a multi-objective problem where the user wants to reduce energy consumption while keeping the occupant's comfort index intact. To address the multi-objective problem this paper proposed an energy control system for a green environment called PMC (Power Management and Control). The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The combination of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is performed to make a fusion methodology to improve the occupant comfort index (OCI) and decrease energy utilization. The proposed framework gives a better OCI when compared with its counterparts, the Ant Bee Colony Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the PMC gives practically the same OCI but consumes less energy. The PMC framework additionally accomplished the ideal OCI (i-e 1) when compared with the existing model, FA–GA (i-e 0.98). The PMC model consumed less energy as compared to existing models such as the ABCKB, GAP, PSO, and AEO. The PMC model consumed a little bit more energy than the SOHP but provided a better OCI. The comparative outcomes show the capability of the PMC framework to reduce energy utilization and improve the OCI. Unlike other existing methodologies except for the AEO framework, the PMC technique is additionally confirmed through a simulation by controlling the indoor environment using actuators, such as fan, light, AC, and boiler. [ABSTRACT FROM AUTHOR]

This paper aims to solve the multi-objective operating planning problem in the radioactive environment. First, a more complicated radiation dose model is constructed, considering difficulty levels at each operating point. Based on this model, the multi-objective operating planning problem is converted to a variant traveling salesman problem (VTSP). Second, with respect to this issue, a novel combinatorial algorithm framework, namely hyper-parameter adaptive genetic algorithm (HPAGA), integrating bio-inspired optimization with reinforcement learning, is proposed, which allows for adaptive adjustment of the hyperparameters of GA so as to obtain optimal solutions efficiently. Third, comparative studies demonstrate the superior performance of the proposed HPAGA against classical evolutionary algorithms for various TSP instances. Additionally, a case study in the simulated radioactive environment implies the potential application of HPAGA in the future. [ABSTRACT FROM AUTHOR]

With the development of digital economy technologies, mobile edge computing (MEC) has emerged as a promising computing paradigm that provides mobile devices with closer edge computing resources. Because of high mobility, unmanned aerial vehicles (UAVs) have been extensively utilized to augment MEC to improve scalability and adaptability. However, with more UAVs or mobile devices, the search space grows exponentially, leading to the curse of dimensionality. This paper focus on the combined challenges of the deployment of UAVs and the task of offloading mobile devices in a large-scale UAV-assisted MEC. Specifically, the joint UAV deployment and task offloading problem is first modeled as a large-scale multiobjective optimization problem with the purpose of minimizing energy consumption while improving user satisfaction. Then, a large-scale UAV deployment and task offloading multiobjective optimization method based on the evolutionary algorithm, called LDOMO, is designed to address the above formulated problem. In LDOMO, a CSO-based evolutionary strategy and a MLP-based evolutionary strategy are proposed to explore solution spaces with different features for accelerating convergence and maintaining the diversity of the population, and two local search optimizers are designed to improve the quality of the solution. Finally, simulation results show that our proposed LDOMO outperforms several representative multiobjective evolutionary algorithms. [ABSTRACT FROM AUTHOR]

Multidimensional sensory data prediction and resource scheduling are paramount challenges in the design of smart cities. This paper delves into the utilization of multi-objective evolutionary algorithms to enhance the accuracy and efficiency of target detection through optimized YOLO_v3 network models. By integrating the YOLO_v3 model with the K-means++ algorithm for Anchor_Box generation, the novel approach exhibits superior adaptability and flexibility, particularly in handling variable-sized feature pattern mappings. This adaptability better caters to the detection of targets of diverse sizes, thus elevating the performance and precision of target detection algorithms. To further scrutinize the YOLO-v3 joint algorithm's performance in urban traffic detection, P-R curves were plotted for various loss types on the NEU-DET dataset. Comparative analysis of these curves highlights the optimized algorithms' superiority in detecting various types of losses in urban model completeness. Additionally, practical application analysis revealed that the optimized monitoring results outperform the detection time of the original YOLO-v3_means++ network model on FP_GA. Notably, post-processing with C-FENCE can reduce average single-frame image detection time to 2.01 seconds, while convolutional degree-level fusion with the BN layer cuts it down to 2.25 seconds. In summary, the FP_GA-based YOLO-v3_means++ network algorithm offers superior detection capabilities, and the multi-objective evolutionary algorithm's optimization of the YOLO-v3 model enhances target detection performance and precision. [ABSTRACT FROM AUTHOR]

This paper proposes a parameter coordination optimization design of a power system stabilizer (PSS) based on an improved quasi-affine transformation evolutionary (QUATRE) algorithm to suppress low-frequency oscillation and improve the dynamic stability of power systems. To begin, the simulated annealing (SA) algorithm randomly updates the globally optimal solution of each QUATRE iteration and matches the inferior solution with a certain probability to escape the local extreme point. This new algorithm is first applied to the power system. Since the damping ratio is one of the criteria with which to measure the dynamic stability of the power system, this paper sets the objective function according to the principle of maximization of the damping coefficient of the electromechanical mode, and uses SA-QUATRE to search a group of global optimal PSS parameter combinations to improve the safety factor of the system as much as possible. Finally, the method's rationality and validity were validated by applying it to the simulation examples of the IEEE 4-machine 2-area system with different operation states. The comparison with the traditional optimization algorithm shows that the proposed method has more advantages for multi-machine PSS parameter coordination optimization, can restrain the low-frequency oscillation of the power system more effectively and can enhance the system's stability. [ABSTRACT FROM AUTHOR]

The Perceptual Crossing Experiment (PCE) has been the object of study for over a decade, and aims at explaining how we perceive, interact with, and understand each other in real-time. In addition to human participant studies, a number of computational models have investigated how virtual agents can solve this task. However, the set of implementation choices that has been explored to date is rather limited, and the large number of variables that can be used make it very difficult to replicate the results. The main objective of this paper is to describe the PCE Simulation Toolkit we have developed and published as an open-source repository on GitHub. We hope that this effort will help make future PCE simulation results reproducible and advance research in the understanding of possible behaviors in this experimental paradigm. At the end of this paper, we present two case studies of evolved agents that demonstrate how parameter choices affect the simulations. [ABSTRACT FROM AUTHOR]