Swarm and Evolutionary Computation最新文献_第5页

A temporal-segmentation-and-fusion-based large-scale constrained multi-objective evolutionary algorithm for coal mine integrated energy systems dispatch 基于时间分割融合的煤矿综合能源系统大规模约束多目标进化调度算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-12 DOI: 10.1016/j.swevo.2025.101959

Xuanxuan Ban , Jing Liang , Dunwei Gong , Yong Zhang , Yaonan Wang , Canyun Dai , Kangjia Qiao , Kunjie Yu

{"title":"A temporal-segmentation-and-fusion-based large-scale constrained multi-objective evolutionary algorithm for coal mine integrated energy systems dispatch","authors":"Xuanxuan Ban , Jing Liang , Dunwei Gong , Yong Zhang , Yaonan Wang , Canyun Dai , Kangjia Qiao , Kunjie Yu","doi":"10.1016/j.swevo.2025.101959","DOIUrl":"10.1016/j.swevo.2025.101959","url":null,"abstract":"<div><div>Under the dual-carbon policy, the optimization of coal mine integrated energy systems (CMIESs) has garnered increasing attention from researchers. However, the characteristics of multiple optimization objectives, strong coupling constraints, and high-dimensional decision variables pose significant challenges for optimization methods. Existing constrained multi-objective optimization algorithms struggle to effectively solve such problems with multiple complex characteristics, and they are prone to getting stuck in local optima. To address this issue, this paper proposes a temporal-segmentation-and-fusion-based large-scale constrained multi-objective evolutionary algorithm, which divides the evolutionary process into two stages. In the subspace optimization stage, the large-scale space is divided into multiple smaller subspaces according to the problem’s temporal divisibility characteristics, then these subspaces are progressively integrated and optimized by using a portion of the computational resources. Once all subspaces are integrated, the original space optimization stage is activated, then the remaining computational resources are employed to search the original large-scale space. In addition, a differential evolution mutation strategy based on dynamic neighborhoods is studied, which effectively balances global exploration and local exploitation, guiding the population toward promising regions in the search space. Finally, the experimental results with several advanced evolutionary algorithms on an actual CMIES dispatch case demonstrate the efficiency of the proposed algorithm.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101959"},"PeriodicalIF":8.2,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935979","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 differential evolution algorithm considering multi-population based on birth & death process and opposition-based learning with condition 基于生灭过程和条件对立学习的多种群差分进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-12 DOI: 10.1016/j.swevo.2025.101966

Xiaolin Yi, Xianfeng Ding, Qian Chen

{"title":"A differential evolution algorithm considering multi-population based on birth & death process and opposition-based learning with condition","authors":"Xiaolin Yi, Xianfeng Ding, Qian Chen","doi":"10.1016/j.swevo.2025.101966","DOIUrl":"10.1016/j.swevo.2025.101966","url":null,"abstract":"<div><div>Differential evolution algorithm with multi-population cooperation and multi-strategy integration (MPMSDE) has been proven to be a better efficient evolutionary algorithm for global optimization. In MPMSDE, dynamic resource allocation and multi-population cooperation are introduced to distribute computational resources rationally. However, there is no effective escape mechanism when MPMSDE falls into a locally optimal solution. Thus, to achieve automatic escape from the local optimum, a differential evolution algorithm considering multi-population based on B&D process and opposition-based learning with condition (MPNBDE) is proposed in this paper. Different from MPMSDE, MPNBDE develops a B&D process and an opposition-based learning mechanism with condition to automatically search high-efficiency for optimal solutions. Also, a Fermi rule in MPNBDE is utilized to control the extent of the maximum computational resource, which is affected by global information. In MPNBDE, a new mutation strategy, ”DE/pbad-to-pbest-gbest-Fermi/1”, is proposed. The new strategy can not only control the extent of information exchanged by the Fermi rule, However, it also can significantly accelerate the convergence of the algorithm. Meanwhile, compared with other differential evolution algorithms, e.g., MPMSDE and SMLDE, our MPNBDE shows better performance in searching optimum, especially in the calculation accuracy and convergence speed on 21 benchmark functions.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101966"},"PeriodicalIF":8.2,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935980","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 interpretable interval-valued wind power prediction system based on multi-objective feature extraction and base model selection with dynamic ensemble 基于多目标特征提取和动态集成基模型选择的可解释区间值风电预测系统

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-09 DOI: 10.1016/j.swevo.2025.101977

Jujie Wang, Yuxuan Lu, Qian Li

{"title":"An interpretable interval-valued wind power prediction system based on multi-objective feature extraction and base model selection with dynamic ensemble","authors":"Jujie Wang, Yuxuan Lu, Qian Li","doi":"10.1016/j.swevo.2025.101977","DOIUrl":"10.1016/j.swevo.2025.101977","url":null,"abstract":"<div><div>Wind power forecasting is essential for resource optimization and sustainable development. However, current forecasting methods mainly rely on single-valued data with limited information, and the black-box nature of artificial intelligence models weakens the interpretability of the prediction results. This paper introduces a new interpretable model for interval-valued wind power forecasting, which enhances prediction accuracy and reliability by leveraging a feature extraction process, a base model selection strategy, and a dynamic ensemble mechanism. First, to address the complexity of interval-valued wind power data, an interpretable multi-objective feature extraction method is designed to distill key trend and fluctuation features, facilitating in-depth learning of features and their relationships. Considering the alignment between features and models, the contribution of each base model to the prediction target is quantified by combining elastic net regression and Shapley additive explanation method, so as to select the base models under different feature sequences in a highly interpretable way. Finally, the base model weights are dynamically adjusted according to the Shapley values to adapt to the time-varying characteristics of individual model accuracy and realize the online update prediction. An empirical study shows that the suggested model outperforms the benchmark model, demonstrating excellent prediction performance and interpretability.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101977"},"PeriodicalIF":8.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923143","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

Including problem-knowledge based modification into a Differential Evolution Algorithm for optimizing planar moment-resisting steel frames 将基于问题知识的修正引入到平面抗弯矩钢框架优化的微分进化算法中

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-09 DOI: 10.1016/j.swevo.2025.101958

Oscar Contreras-Bejarano , Jesús Daniel Villalba-Morales , Diego Lopez-Garcia

{"title":"Including problem-knowledge based modification into a Differential Evolution Algorithm for optimizing planar moment-resisting steel frames","authors":"Oscar Contreras-Bejarano , Jesús Daniel Villalba-Morales , Diego Lopez-Garcia","doi":"10.1016/j.swevo.2025.101958","DOIUrl":"10.1016/j.swevo.2025.101958","url":null,"abstract":"<div><div>The Differential Evolution Algorithm (DEA) has been demonstrated to be capable of effectively addressing engineering challenges, although its performance varies considerably when applied to different problems. Customizing the algorithm to the specific characteristics of a given problem has been identified as a valid strategy to enhance its effectiveness and reliability. In this study, a tailored version of the DEA is proposed for the optimization of planar Moment-Resisting Steel Frames (MRSFs) subjected to static loads. A diverse set of heuristics and techniques were incorporated, including advanced strategies for parameter control, initialization, mutation operators, crossover operators, diversity conservation, constraints handling, and dynamic population management. To evaluate the performance of the proposed heuristics and techniques, 7800 DEA configurations were applied to the optimization of seven representative MRSFs. Results indicate that through problem-specific modifications the DEA is highly likely to identify the optimal solutions. By emphasizing both computational efficiency and solution quality, this research provides valuable insights into enhanced applicability of the DEA to structural optimization problems. It is shown that a customized algorithm is a reliable, effective, and robust tool to optimize MRSFs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101958"},"PeriodicalIF":8.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923144","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

Revolutionizing Wireless Rechargeable Sensor Networks: Speed Optimization-based Charging Scheduling Scheme (SOCSS) for efficient multi-node energy transfer 革命性的无线可充电传感器网络：基于速度优化的充电调度方案（SOCSS），用于高效的多节点能量传输

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-09 DOI: 10.1016/j.swevo.2025.101961

Riya Goyal, Abhinav Tomar

{"title":"Revolutionizing Wireless Rechargeable Sensor Networks: Speed Optimization-based Charging Scheduling Scheme (SOCSS) for efficient multi-node energy transfer","authors":"Riya Goyal, Abhinav Tomar","doi":"10.1016/j.swevo.2025.101961","DOIUrl":"10.1016/j.swevo.2025.101961","url":null,"abstract":"<div><div>Benefiting from the breakthrough of Wireless Energy Transfer (WET) technology, scheduling multiple Mobile Chargers (MCs) to charge sensor nodes can significantly prolong the lifetime of Wireless Rechargeable Sensor Networks (WRSNs). While previous studies have primarily focused on on-demand recharging within WRSNs, more consideration must be given to utilizing multi-node energy transfer with optimal charging locations to devise efficient charging schedules for requesting sensor nodes. Moreover, existing approaches assume a constant travel speed for MCs and utilize omnidirectional WET, leading to increased energy consumption for MCs and consequently affecting overall charging efficiency. To address these challenges, we propose a novel Speed Optimization-based Charging Scheduling Scheme (SOCSS) for multiple MCs in WRSNs. The initial phase of SOCSS involves clique-based network partitioning to identify minimum cliques and determine optimal charging locations to perform efficient multi-node energy transfer for sensor nodes. The subsequent phase encompasses scheduling and path planning, where the charging schedule is established using efficient Quantum-inspired Particle Swarm Optimization. By integrating speed optimization with the charging schedule, the energy consumption of the MCs is minimized, leading to cost-effective planning of the charging path for energy-constrained MCs. Extensive simulations are conducted to showcase the supremacy of SOCSS across a range of network parameters compared to prior art. In particular, SOCSS has achieved an impressive average reduction of 36.2% in the number of stopping points for MCs, a remarkable 38.9% decrease in the total travel distance, and a 15.7% reduction in the charging delay.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101961"},"PeriodicalIF":8.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923142","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 matheuristic-based self-learning evolutionary algorithm for lot streaming hybrid flow shop group scheduling with limited auxiliary modules 基于数学的有限辅助模块的批流混合流水车间群调度自学习进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-08 DOI: 10.1016/j.swevo.2025.101965

Hongxia Tan , Min Zhou , Liping Zhang , Zikai Zhang , Yingli Li , Zixiang Li

{"title":"A matheuristic-based self-learning evolutionary algorithm for lot streaming hybrid flow shop group scheduling with limited auxiliary modules","authors":"Hongxia Tan , Min Zhou , Liping Zhang , Zikai Zhang , Yingli Li , Zixiang Li","doi":"10.1016/j.swevo.2025.101965","DOIUrl":"10.1016/j.swevo.2025.101965","url":null,"abstract":"<div><div>Group scheduling enhances production flexibility and efficiency in mass customization However, it overlooks differences of due dates in customized orders and functional/quantity constraints of molds. Therefore, lot streaming and module assignment strategies are needed. To address this, this paper investigates the lot streaming hybrid flow shop group scheduling with limited auxiliary module constraints(HFGSP_LSAM). To minimize the total weighted tardiness and makespan, a new mixed integer linear programming model and a matheuristic-based self-learning evolutionary algorithm(MSEA) are proposed. This algorithm develops a new matheuristic-based hybrid initialization to generate better initial solutions. A double layer self-learning evolution is developed to collaborate operators which include six knowledge-based local search operators and six global crossover operators. The experimental study, based on 360 small and 960 large instances, demonstrates that the matheuristic-based hybrid initialization and double layer self-learning evolution can enhance 84% and 13% performance of MSEA, as well as the proposed MSEA is superior to other well known algorithms in solving HFGSP_LSAM. An industrial case study is conducted to confirm the superiority of MSEA and provide two recommendations for managers to balance production efficiency and due dates.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101965"},"PeriodicalIF":8.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918417","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

Multimodal multi-objective optimization via multi-operator adaptation and clustering-based environmental selection 基于多算子自适应和聚类环境选择的多模态多目标优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-06 DOI: 10.1016/j.swevo.2025.101962

Xinyi Wu , Fei Ming , Wenyin Gong , Bolin Liao , Yuanyuan Guo

{"title":"Multimodal multi-objective optimization via multi-operator adaptation and clustering-based environmental selection","authors":"Xinyi Wu , Fei Ming , Wenyin Gong , Bolin Liao , Yuanyuan Guo","doi":"10.1016/j.swevo.2025.101962","DOIUrl":"10.1016/j.swevo.2025.101962","url":null,"abstract":"<div><div>In real world applications, multimodal multi-objective optimization problems are common, addressing which can offer decision makers multiple choices to accommodate varying scenarios. Many researchers have been focusing on this kind of problem, leading to the development of numerous multimodal multi-objective evolutionary optimization algorithms (MMOEAs). However, most existing MMOEAs employ a fixed operator to generate offspring. For different types of problems, the use of hybrid operators can take advantage of their distinct features in reproduction to produce more valuable individuals. To address this issue, we propose an innovative algorithm that integrates two operators collaboratively and dynamically adjusts the proportion of offspring generated by each operator based on its performance throughout the evolution process evaluated by the survival rate. In addition, to better balance the diversity, the proposed algorithm devises a novel clustering method, which clusters the population in the decision space. Then, individuals within the same cluster with better performance in the objective space are able to survive. We evaluate our algorithm against seven representative MMOEAs on two widely used benchmark problems and real-world problems. The experimental results confirm the superior performance and robustness of our approach on both benchmark and real-world problems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101962"},"PeriodicalIF":8.2,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143905831","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

Learning-driven memetic algorithm for solving integrated distributed production and transportation scheduling problem 基于学习驱动的模因算法求解集成分布式生产运输调度问题

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-04 DOI: 10.1016/j.swevo.2025.101945

Shicun Zhao, Hong Zhou

{"title":"Learning-driven memetic algorithm for solving integrated distributed production and transportation scheduling problem","authors":"Shicun Zhao, Hong Zhou","doi":"10.1016/j.swevo.2025.101945","DOIUrl":"10.1016/j.swevo.2025.101945","url":null,"abstract":"<div><div>Production and transportation scheduling are critical components in modern manufacturing. However, existing studies on their integrated optimization are still limited, and most of them focus on the integration of production and local logistics within the shop. Different from previous investigations, this paper considers the integration of production scheduling with transportation across enterprises, which is especially typical and significant for production management in the large-scale distributed manufacturing environment. Considering the energy-aware orientation and production performance, the problem is formulated as a bi-objective integrated production planning and transportation scheduling problem for distributed flexible job shops. A mixed-integer linear programming model is developed to describe the considered problem with the aim of optimizing customer satisfaction and total energy consumption. To address this problem, an enhanced memetic algorithm with a reinforcement learning-driven breeding mechanism (RDMA) is proposed. Unlike existing literature that uses reinforcement learning to adjust parameters or select operators, RDMA marks the initial use of reinforcement learning to recommend the most suitable parents for breeding offspring. Additionally, a knowledge-driven adaptive variable neighborhood search is designed to make incremental improvements to the best solutions and continuously enhance RDMA’s local search performance. Comparative results highlight the benefit of the reinforcement learning-based breeding mechanism and demonstrate the superiority of RDMA over major existing state-of-the-art algorithms. Moreover, experimental analysis indicates that each component in RDMA positively affects search performance, and their collaboration yields the best results.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101945"},"PeriodicalIF":8.2,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903833","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

Listwise ranking predictor for evolutionary neural architecture search 进化神经结构搜索的列表排序预测器

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-03 DOI: 10.1016/j.swevo.2025.101956

Nan Li , Lianbo Ma , Rui Wang , Shi Cheng , Yanan Sun , Bing Xue , Mengjie Zhang

{"title":"Listwise ranking predictor for evolutionary neural architecture search","authors":"Nan Li , Lianbo Ma , Rui Wang , Shi Cheng , Yanan Sun , Bing Xue , Mengjie Zhang","doi":"10.1016/j.swevo.2025.101956","DOIUrl":"10.1016/j.swevo.2025.101956","url":null,"abstract":"<div><div>In evolutionary neural architecture search (ENAS), the accuracy predictors (i.e., regression models) have been successfully applied to save computational costs for the evaluation of network architectures. However, the accuracy of these predictors is largely limited by the small amount of evaluated architectures that may be difficult to obtain. Such accuracy predictors with prediction bias often lead to an inaccurate ranking, misleading the selection of ENAS. To alleviate the above limitations, we design an efficient and novel listwise ranking predictor (LRP) for ENAS to directly predict the ranking of each architecture instead of the numerical accuracy value of each architecture. Specifically, the training data is constructed by the proposed random encoding-combination (REC) strategy, which can generate substantial training data using the small number of evaluated architectures (data level). These specially constructed training data are used to train LRP, which can convert the complex regression task into a ranking task to reduce ranking bias (model level). The proposed NAS method is compared with state-of-the-art NAS methods on widely-used benchmark datasets and practical application. Experimental results demonstrate that LRP can alleviate the ranking disorder problem and outperform others in terms of both effectiveness and efficiency.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101956"},"PeriodicalIF":8.2,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902523","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 coevolutionary algorithm for constrained multi-objective optimization with dynamic relaxation 动态松弛约束多目标优化的协同进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-01 DOI: 10.1016/j.swevo.2025.101954

Yongchao Li , Heming Jia , Hongguang Li

{"title":"A coevolutionary algorithm for constrained multi-objective optimization with dynamic relaxation","authors":"Yongchao Li , Heming Jia , Hongguang Li","doi":"10.1016/j.swevo.2025.101954","DOIUrl":"10.1016/j.swevo.2025.101954","url":null,"abstract":"<div><div>To effectively address constrained multi-objective problems, algorithms need to strike a balance between objectives and constraints. This article introduces a method that utilizes two separate populations to investigate the exploration of the constrained Pareto front (CPF) and the unconstrained Pareto front (UPF). The fitness of each population is evaluated based on the information entropy of their positions, and suitable evolutionary operators are employed to improve solution quality in terms of convergence and diversity. Moreover, by adaptively relaxing constraint conditions, the auxiliary population can traverse large infeasible domains, thereby enhancing solution diversity. In the initial stages, the auxiliary population evolves alongside the main population, bringing it close to the CPF and minimizing computational resource wastage. A tournament environment selection model based on a dynamic relaxation (DR) function is utilized in the later stages, helping the auxiliary population relax constraints, retain promising solutions, and augment diversity. In addition, an entropy selection evolutionary strategy was designed to address the problem of populations easily falling into local optima during the evolution process. By calculating the entropy information of the population, the current state of the population can be determined, and then appropriate operators can be selected to enable the population to effectively escape from local optimal solutions. Compared against seven state-of-the-art algorithms, demonstrate that the proposed constrained multi-objective optimization evolutionary algorithm (CMOEA) surpasses the performance of existing CMOEAs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101954"},"PeriodicalIF":8.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891942","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