Swarm and Evolutionary Computation最新文献_第10页

A multiple surrogate-assisted hybrid evolutionary feature selection algorithm 一种多代理辅助混合进化特征选择算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101809

Wan-qiu Zhang , Ying Hu , Yong Zhang , Zi-wang Zheng , Chao Peng , Xianfang Song , Dunwei Gong

{"title":"A multiple surrogate-assisted hybrid evolutionary feature selection algorithm","authors":"Wan-qiu Zhang , Ying Hu , Yong Zhang , Zi-wang Zheng , Chao Peng , Xianfang Song , Dunwei Gong","doi":"10.1016/j.swevo.2024.101809","DOIUrl":"10.1016/j.swevo.2024.101809","url":null,"abstract":"<div><div>Feature selection (FS) is an important data processing technology. However, existing FS methods based on evolutionary computation have still the problems of “curse of dimensionality” and high computational cost, with the increase of the number of feature and/or the size of instance. In view of this, the paper proposes a multiple surrogate-assisted hybrid evolutionary feature selection (MSa-HEFS). Two kinds of surrogates (i.e., objective regression surrogate and sample surrogate) and two kinds of FS methods (i.e., filter and wrapper) are integrated into MSa-HEFS to improve its performance. Firstly, an ensemble filter FS method is designed to reduce the search space of subsequent wrapper evolutionary FS method. Secondly, in the proposed evolutionary FS method, a dual-surrogate-assisted hierarchical individual evaluation mechanism is developed to reduce the evaluation cost on feature subsets, an online management and update strategy is used to adaptively choose appropriate surrogates for individuals. The proposed algorithm is applied to 12 typical datasets and compared with 4 state-of-the-art FS algorithms. Experimental results show that MSa-HEFS can obtain good feature subsets at the smallest computational cost on all datasets. MSa-HEFS source code is available on Github at <span><span>https://github.com/ZZW-zq/MSa-HEFS-/tree/master</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101809"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cost optimization, reliability, and MTTF analysis for failed excavators in hydraulic repair center using queueing theory 基于排队理论的液压维修中心故障挖掘机成本优化、可靠性及MTTF分析

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101824

Khushbu S. Antala, Sudeep Singh Sanga

{"title":"Cost optimization, reliability, and MTTF analysis for failed excavators in hydraulic repair center using queueing theory","authors":"Khushbu S. Antala, Sudeep Singh Sanga","doi":"10.1016/j.swevo.2024.101824","DOIUrl":"10.1016/j.swevo.2024.101824","url":null,"abstract":"<div><div>In the present study, we utilize the application of queues at construction sites where excavators are used extensively. Excavators are prone to failures requiring timely repairs and maintenance. We establish a hydraulic repair center (HRC) to repair and maintain these failed excavators. The HRC is equipped with a dedicated hydraulic hose crimper (HHC) machine, which acts as the server providing repairs to the arriving failed excavators, referred to as customers. Two types of excavators are considered: crawler excavator (CE) and mini excavator (ME), with ME being given priority in repair jobs over CE. To address realistic situations, various queueing characteristics are incorporated, including a non-preemptive priority rule, a retrial orbit, etc. First, we develop a mathematical model by considering the arrival of excavators at the HRC following a Poisson process, with repair times adhering to exponential distributions. We construct the Markov model by formulating time-dependent differential-difference equations for each system state. These equations are then solved using a matrix method based on spectral theory to develop the corresponding probability distributions. Second, we establish several expressions of queueing and reliability indices. Third, a nonlinear cost function is formulated, and optimized using particle swarm optimization (PSO) algorithm and the bat algorithm (BA).</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101824"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structural bias in metaheuristic algorithms: Insights, open problems, and future prospects 元启发式算法中的结构性偏差：见解、开放问题和未来展望

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101812

Kanchan Rajwar , Kusum Deep

{"title":"Structural bias in metaheuristic algorithms: Insights, open problems, and future prospects","authors":"Kanchan Rajwar , Kusum Deep","doi":"10.1016/j.swevo.2024.101812","DOIUrl":"10.1016/j.swevo.2024.101812","url":null,"abstract":"<div><div>This paper addresses a critical issue of structural bias in metaheuristic algorithms, a key factor that often hinders their effectiveness in solving complex optimization problems. Such biases, typically resulting from the design of algorithmic operators and solution construction processes, can lead to a decrease in performance over time. Despite its importance, structural bias is little understood and rarely explored. Moreover, the theoretical framework for structural bias in this context is notably underdeveloped. To the best of our knowledge, no comprehensive review of structural bias in metaheuristic algorithms is available to date. Consequently, this study is subjected to a thorough literature review, providing the mathematical definition of structural bias, the theoretical background, and an extensive analysis of its various forms within metaheuristic algorithms. This paper discusses structural bias in several metaheuristic algorithms, including the Genetic Algorithm, Particle Swarm Optimization, Differential Evolution, and Ant Colony Optimization. Methodologies for identifying structural bias, currently scattered across several studies, are categorized into four classes and discussed through the implementation of Particle Swarm Optimization, highlighting their advantages and limitations. Additionally, five critical open problems are identified, and essential research directions for future exploration are outlined. As the first comprehensive review of structural bias – an issue gaining increasing attention – this work is expected to serve as a vital resource for algorithm designers and the research community.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101812"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep reinforcement learning assisted surrogate model management for expensive constrained multi-objective optimization 深度强化学习辅助代理模型管理的昂贵约束多目标优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101817

Shuai Shao, Ye Tian, Yajie Zhang

{"title":"Deep reinforcement learning assisted surrogate model management for expensive constrained multi-objective optimization","authors":"Shuai Shao, Ye Tian, Yajie Zhang","doi":"10.1016/j.swevo.2024.101817","DOIUrl":"10.1016/j.swevo.2024.101817","url":null,"abstract":"<div><div>Expensive constrained multi-objective optimization problems (ECMOPs) exist in a wide variety of applications from industrial processes to engineering systems. When solving ECMOPs, with only a limited number of function evaluations available, a common approach is to substitute the real function evaluations with more affordable evaluations provided by computationally efficient surrogate models. However, existing surrogate assisted evolutionary algorithms (SAEAs) exhibit poor versatility in handling various ECMOPs, as they only use a constant surrogate modeling scheme or switch the modeling schemes with expert knowledge. To address the dilemma in surrogate modeling, this paper proposes a deep reinforcement learning assisted evolutionary algorithm, which operates on two key issues. First, multiple surrogate models are employed to learn the approximate function of an ECMOP using previously evaluated solutions during the evolutionary process. Second, a deep reinforcement learning method is employed to learn the optimal surrogate model management strategy based on evolutionary experience, selecting the most suitable surrogate modeling scheme for the current generation. Experimental evaluations on a large number of expensive problems demonstrate that the proposed algorithm has a significant effect compared with state-of-the-art competitors.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101817"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A decompose-and-learn multi-objective algorithm for scheduling large-scale earth observation satellites 大型对地观测卫星调度的分解学习多目标算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101792

Jing Qi , Min Hu , Lining Xing

{"title":"A decompose-and-learn multi-objective algorithm for scheduling large-scale earth observation satellites","authors":"Jing Qi , Min Hu , Lining Xing","doi":"10.1016/j.swevo.2024.101792","DOIUrl":"10.1016/j.swevo.2024.101792","url":null,"abstract":"<div><div>In recent studies, task scheduling problems of earth observation satellites (EOSs) still encounter large difficulties when they meet the increasing number of satellites. Moreover, multiple objectives and increasing number of tasks must be considered in business affairs. To effectively address these issues, considering satellite orbits as independent resources, a multi-objective algorithm is tailored for earth observation satellites scheduling problems (EOSSPs) in this paper. The algorithm includes a novel dynamic learning task allocation mechanism and a bidirectional sorting strategy with global patching method. Specifically, the mechanism works as an evolution operator to allocate tasks to appropriate orbits thus reducing problem complexity and the strategy is tailored to schedule observation windows to corresponding tasks. With the mechanism, original EOSSP can be decomposed into subproblems through the task allocation mechanism, with each task assigned to an corresponding orbit using adaptively updating guideline values. The decision space of a subproblem is limited within one single orbit, which will vastly decrease the complexity of the original large-scale EOSSP. Then, the bidirectional sorting strategy will schedule specific observation windows of each orbit to the candidate tasks. Since the algorithm is proposed to solve large-scale EOSSPs with multi-orbit and multi-objective, it is referred here as LS2MO-SS. Finally, extensive experiments are conducted on ten large-scale problems with different tasks to evaluate the performance of the multi-objective algorithm, the proposed evolution operator, and the bidirectional sorting strategy. The comparative results indeed validate the effectiveness of the proposed algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101792"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Nonlinear Dimensionality Reduction Search Improved Differential Evolution for large-scale optimization 一种非线性降维搜索改进的差分进化大规模优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101832

Yifei Yang , Haotian Li , Zhenyu Lei , Haichuan Yang , Jian Wang

{"title":"A Nonlinear Dimensionality Reduction Search Improved Differential Evolution for large-scale optimization","authors":"Yifei Yang , Haotian Li , Zhenyu Lei , Haichuan Yang , Jian Wang","doi":"10.1016/j.swevo.2024.101832","DOIUrl":"10.1016/j.swevo.2024.101832","url":null,"abstract":"<div><div>Large-scale optimization problems present significant challenges due to the high dimensionality of the search spaces and the extensive computational resources required. This paper introduces a novel algorithm, Nonlinear Dimensionality Reduction Enhanced Differential Evolution (NDRDE), designed to address these challenges by integrating nonlinear dimensionality reduction techniques with differential evolution. The core innovation of NDRDE is its stochastic dimensionality reduction strategy, which enhances population diversity and improves the algorithm’s exploratory capabilities. NDRDE also employs a spherical search method to maximize the obliteration of directional information, thus increasing randomness and improving the exploration phase. The algorithm dynamically adjusts the dimensionality of the search space, leveraging a combination of high-dimensional precision search and low-dimensional exploratory search. This approach not only reduces the computational burden but also maintains a high level of accuracy in finding optimal solutions. Extensive experiments on the IEEE CEC large-scale global optimization benchmark problems, including CEC2010 and CEC2013, demonstrate that NDRDE significantly outperforms several state-of-the-art algorithms, showcasing its superiority in tackling large-scale optimization problems. The code for NDRDE will be made publicly available at <span><span>https://github.com/louiseklocky</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101832"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An evolutionary algorithm driving by dimensionality reduction operator and knowledge model for the electric vehicle routing problem with flexible charging strategy 基于降维算子和知识模型的电动汽车柔性充电路径问题的进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101814

Bihao Yang, Teng Ren, Huijuan Yu, Jie Chen, Yaya Wang

{"title":"An evolutionary algorithm driving by dimensionality reduction operator and knowledge model for the electric vehicle routing problem with flexible charging strategy","authors":"Bihao Yang, Teng Ren, Huijuan Yu, Jie Chen, Yaya Wang","doi":"10.1016/j.swevo.2024.101814","DOIUrl":"10.1016/j.swevo.2024.101814","url":null,"abstract":"<div><div>The digital economy and digital technology are promoting the integrated development of industry and digital, forming a new path for industrial upgrading and building a new development pattern.In today's context of digital economy and green transformation, it is a challenging optimization problem to scientifically plan the logistics routes of electric vehicles (EVs) when taking charging strategies into consideration. Aiming at the drawback of supposing a fixed charging rate in the traditional EV routing problems (EVRPs), the charging data of a type of mainstream EVs were collected and the instantaneous charging power was simulated in the real scenario. To solve problems of the fixed charge timing and charged energy in traditional EVRP models and partial charging strategies, a new EVRP model considering the flexible charging strategy (EVRP-FCS) by taking the charged energy as one of the decision variables. To effectively solve the model and fully search in the solution space, an improved evolutionary algorithm was proposed. The performance advantages of the algorithm are determined by comparison of 22 groups of large-scale experimental examples. The experimental results have demonstrated the performance advantages of the algorithm.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101814"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Knee-oriented expensive many-objective optimization via aggregation-dominance: A multi-task perspective 基于聚合优势的面向膝盖的昂贵多目标优化：多任务视角

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101813

Junfeng Tang , Handing Wang , Yaochu Jin

{"title":"Knee-oriented expensive many-objective optimization via aggregation-dominance: A multi-task perspective","authors":"Junfeng Tang , Handing Wang , Yaochu Jin","doi":"10.1016/j.swevo.2024.101813","DOIUrl":"10.1016/j.swevo.2024.101813","url":null,"abstract":"<div><div>Given the costs to implement whole Pareto optimal solutions, users often prefer solutions of interest, like knee points, which represent naturally preferred solutions without a specific bias. Recent surrogate-assisted multi-objective evolutionary algorithms (SAMOEAs) incorporating knee identification techniques have been suggested, but most of them cannot find knee solutions for expensive many-objective optimization problems. This work proposes a Kriging-assisted evolutionary multi-task algorithm with aggregation-dominance. The aggregation-dominance approach identifies knee points on an estimated Pareto front, from which subproblems are created and solved in parallel via Kriging-assisted multi-task optimization for guiding search knee solutions. Additionally, our proposed infill solutions selection strategy focuses on re-evaluating solutions converging in regions of interest. Experimental results on knee-oriented benchmark problems show that our algorithm outperforms state-of-the-art methods, with aggregation-dominance surpassing five existing knee identification techniques. We also validate the algorithm’s performance on the portfolio allocation problem.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101813"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating asynchronous advantage actor–critic (A3C) and coalitional game theory algorithms for optimizing energy, carbon emissions, and reliability of scientific workflows in cloud data centers 集成异步优势参与者批评（A3C）和联合博弈论算法，优化云数据中心的能源、碳排放和科学工作流程的可靠性

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101756

Mustafa Ibrahim Khaleel

{"title":"Integrating asynchronous advantage actor–critic (A3C) and coalitional game theory algorithms for optimizing energy, carbon emissions, and reliability of scientific workflows in cloud data centers","authors":"Mustafa Ibrahim Khaleel","doi":"10.1016/j.swevo.2024.101756","DOIUrl":"10.1016/j.swevo.2024.101756","url":null,"abstract":"<div><div>The growth of workflow as a service (WFaaS) has become more intricate with the increasing variety and number of workflow module applications and expanding computing resources. This complexity leads to higher energy consumption in data centers, negatively impacting the environment and extending processing times. Striking a balance between reducing energy and carbon emissions and maintaining scheduling reliability is challenging. While deep reinforcement learning (DRL) approaches have shown significant success in workflow scheduling, they require extensive training time and data due to application homogeneity and sparse rewards, and they do not always guarantee effective convergence. On the other hand, experts have developed various scheduling policies that perform well for different optimization goals, but these heuristic strategies lack adaptability to environmental changes and specific workflow optimization. To address these challenges, an enhanced asynchronous advantage actor–critic (A3C) method combined with merge-and-split-based coalitional game theory is proposed. This approach effectively guides DRL learning in large-scale dynamic scheduling issues using optimal policies from the expert pool. The merge-and-split-based method prioritizes computing nodes based on their preemptive characteristics and resource heterogeneity, ensuring reliability-aware workflow scheduling that maps applications to computing resources while considering the dynamic nature of energy costs and carbon footprints. Experiments on real and synthesized workflows show that the proposed algorithm can learn high-quality scheduling policies for various workflows and optimization objectives, achieving energy efficiency improvements of 7.65% to 19.32%, carbon emission reductions of 3.13% to 14.76%, and reliability enhancements of 17.22% to 41.65%.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101756"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multi-population competitive evolutionary algorithm based on genotype preference for multimodal multi-objective optimization 基于基因型偏好的多种群竞争进化算法多模态多目标优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101826

Keyu Zhong , Fen Xiao , Xieping Gao

{"title":"A multi-population competitive evolutionary algorithm based on genotype preference for multimodal multi-objective optimization","authors":"Keyu Zhong , Fen Xiao , Xieping Gao","doi":"10.1016/j.swevo.2024.101826","DOIUrl":"10.1016/j.swevo.2024.101826","url":null,"abstract":"<div><div>Many existing multimodal multi-objective evolutionary algorithms (MMOEAs) exhibit poor performance in addressing multimodal multi-objective optimization problems (MMOPs), mainly due to limited genetic diversity in environmental selection. In this paper, we propose a multi-population competitive evolutionary algorithm based on genotype preference (MPCEA-GP) to solve MMOPs. Firstly, we propose a population selection strategy based on genotype preference to maintain the genetic diversity of the population. This strategy utilizes the spectral radius to assess the overall convergence quality of the population, rather than evaluating each individual separately, and favors selecting the population with the minimum spectral radius, thereby preserving the genotypes of both optimal and suboptimal individuals. Secondly, to address the challenge of diminished genetic diversity during the evolutionary process, we incorporate historical survival population with substantial genetic diversity into the competition between parent and offspring, and preferentially select individuals with significant genotype differences to recombine into a new population. By merging two selected populations, a joint population with sufficient genetic diversity is constructed. Finally, a genotype-phenotype-based fitness criterion is devised to evaluate the fitness of individuals. This criterion not only compares genotypes using the Pareto dominance principle but also concurrently considers both genotype and phenotype diversity, aiding the population in more precisely identifying individuals with both good convergence and diversity. Empirical results show that MPCEA-GP outperforms state-of-the-art MMOEAs for 40 chosen benchmark functions and two complex real-world applications.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101826"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0