Swarm and Evolutionary Computation最新文献_第3页

Block optimization and switchable hybrid clustering for multimodal multiobjective evolutionary optimization with shifted local Pareto front 局部Pareto前沿移位的多模态多目标进化优化的块优化和可切换混合聚类

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-09-01 DOI: 10.1016/j.swevo.2025.102151

Yu Zhang, Wang Hu

{"title":"Block optimization and switchable hybrid clustering for multimodal multiobjective evolutionary optimization with shifted local Pareto front","authors":"Yu Zhang, Wang Hu","doi":"10.1016/j.swevo.2025.102151","DOIUrl":"10.1016/j.swevo.2025.102151","url":null,"abstract":"<div><div>Existing multimodal multiobjective evolutionary algorithms (MMOEAs) often struggle with translated test functions and fail to effectively identify and maintain local Pareto fronts (PFs) due to the lack of niche-based strategies. To overcome these limitations, a novel two-stage MMOEA termed MMOEA-BH is proposed with block optimization and switchable hybrid clustering. Key innovations include a block optimization strategy utilizing adaptive region stretching and regression-based dimensional analysis, and a switchable hybrid clustering method combining affinity propagation, k-means, and density-based spatial clustering of applications with noise (DBSCAN). These innovations enable MMOEA-BH to effectively address translated test functions and maintain both global and local niches in decision and objective spaces. To address the lack of robust evaluation methods for MMOEAs when solving translated MMOPs, a new set of shifted multimodal multiobjective functions (SMMF) is introduced by translating the existing MMOPs. Experimental results, including comparisons with state-of-the-art algorithms, ablation studies on block optimization, and sensitivity analyses on key parameters, demonstrate that MMOEA-BH outperforms existing algorithms on these SMMF functions. This highlights the efficacy of the proposed block optimization and switchable hybrid clustering strategies in solving MMOPs with translation characteristics.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102151"},"PeriodicalIF":8.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921843","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

Reinforcement knowledge sharing assisted two-archive evolutionary algorithm for many-objective optimization 强化知识共享辅助双档案进化算法的多目标优化

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-31 DOI: 10.1016/j.swevo.2025.102139

Qing Xu , Shuzheng Xie , Ning Yang , Ying Huang , Shaochang Nie , Wei Li

{"title":"Reinforcement knowledge sharing assisted two-archive evolutionary algorithm for many-objective optimization","authors":"Qing Xu , Shuzheng Xie , Ning Yang , Ying Huang , Shaochang Nie , Wei Li","doi":"10.1016/j.swevo.2025.102139","DOIUrl":"10.1016/j.swevo.2025.102139","url":null,"abstract":"<div><div>In many-objective optimization problems (MaOPs), algorithms are challenged in terms of convergence pressure and exploration of the complete Pareto front (PF) as the number of objectives increases. The two-archive mechanism currently offers a novel perspective to address this issue. However, most existing two-archive-based many-objective optimization algorithms focus on independently updating the convergence archive (CA) and diversity archive (DA), while paying less attention to deeper cooperation between the two archives. To facilitate deeper cooperation, this paper proposes a reinforcement knowledge sharing assisted two-archive evolutionary algorithm for many-objective optimization (RKS-TAEA). In RKS-TAEA, a generalized SDE indicator (SDEp) and a new shift-based indicator (SBI) are proposed respectively for the update of CA and DA. SDEp could well maintain the properties of the original SDE indicator on estimating population convergence, while SBI could comprehensively assess not only diversity but also convergence of candidate solutions. Both SDEp and SBI could flexibly fit MaOPs with different PF geometries once the <span><math><mi>p</mi></math></span>-value is properly set for the Minkowski distance calculated in the two indicators. Thereafter, a reinforcement knowledge-sharing mechanism is proposed to derive the <span><math><mi>p</mi></math></span>-value from the knowledge factor that is learnt by fitting the PF geometry of the MaOP generation by generation. The reinforcement knowledge-sharing mechanism achieves deeper cooperation between the two archives, which ensures that RKS-TAEA could adaptively fit complex MaOPs that have different PF geometries. Comprehensive experiments on four benchmark test suites and five real-world MaOPs demonstrate that RKS-TAEA is more competitive in comparison with some state-of-the-art many-objective evolutionary algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102139"},"PeriodicalIF":8.5,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920103","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 population game-based knowledge transfer strategy for constrained multi-objective optimization 基于群体博弈的约束多目标优化知识转移策略

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-31 DOI: 10.1016/j.swevo.2025.102146

Shaoning Liu , Jian Feng , Shengxiang Yang , Jun Zheng

{"title":"A population game-based knowledge transfer strategy for constrained multi-objective optimization","authors":"Shaoning Liu , Jian Feng , Shengxiang Yang , Jun Zheng","doi":"10.1016/j.swevo.2025.102146","DOIUrl":"10.1016/j.swevo.2025.102146","url":null,"abstract":"<div><div>In constrained multi-objective optimization problems (CMOPs), complex constraints may result in narrow feasible regions or cause the Pareto front to lie on constraint boundaries, which significantly increases the difficulty of locating feasible solutions within limited computational resources. Evolutionary multitasking optimization algorithms promote the optimization of the main task by introducing auxiliary tasks. However, even when the contributions of these auxiliary tasks diminish, they continue to consume computational resources. To address this issue, this study proposes a population game-based multitasking coevolutionary algorithm. The algorithm models the original CMOP as a multitasking optimization problem comprising two tasks. Specifically, the target task explores the feasible region of the original CMOP by evolving a population. Meanwhile, the source task is activated dynamically through a population game mechanism, aiming to explore potential feasible regions by relaxing the constraints. Through knowledge transfer, the supplementary evolutionary directions obtained from the source task provide unexplored paths for the target task, guiding the population to approach the Pareto front from both feasible and infeasible directions. Comprehensive experiments were performed on four benchmark suites. The experimental results demonstrated that the proposed algorithm exhibited competitive or superior performance compared with eight state-of-the-art algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102146"},"PeriodicalIF":8.5,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920104","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 knowledge transfer-based strategy for constrained multiobjective optimization 基于知识转移的约束多目标优化策略

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-29 DOI: 10.1016/j.swevo.2025.102111

Shaoning Liu , Jian Feng , Shengxiang Yang , Jun Zheng , Yu Yao

{"title":"A knowledge transfer-based strategy for constrained multiobjective optimization","authors":"Shaoning Liu , Jian Feng , Shengxiang Yang , Jun Zheng , Yu Yao","doi":"10.1016/j.swevo.2025.102111","DOIUrl":"10.1016/j.swevo.2025.102111","url":null,"abstract":"<div><div>The complex constraints in constrained multiobjective optimization problems may cause the Pareto front to be distributed on disconnected feasible boundaries. Most existing evolutionary algorithms encounter challenges in obtaining the entire Pareto front due to inappropriate cooperation between the populations. The ideology of knowledge transfer provides inspiration for addressing complex optimization problems. Based on this, this paper proposes a knowledge transfer-based coevolutionary algorithm, which adopts the idea of divide-and-conquer and two combined into one. The algorithm derives the original constrained multiobjective optimization problem into two problems, both of which share the same optimization objective but follow distinct search trajectories. Specifically, one problem focuses on global search, while the other emphasizes local search. A knowledge transfer strategy is proposed to achieve the exchange of complementary information between these two problems in the evolutionary directions. This strategy assists in solving the derived problem by transferring promising individuals that remain undiscovered in the search trajectories. The optimal solution of the original constrained multiobjective optimization problem is obtained. Experiments conducted on 56 benchmark problems show superior or competitive performance compared with 11 state-of-the-art algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102111"},"PeriodicalIF":8.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144916297","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

Discrete optimization algorithms for distributed bi-agent flowshop scheduling with release dates 带发布日期的分布式双代理流水车间调度离散优化算法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-29 DOI: 10.1016/j.swevo.2025.102101

Danyu Bai , Wenjia Zheng , Chenbo Zang , Jie Yang , Chin-Chia Wu , Hu Qin

{"title":"Discrete optimization algorithms for distributed bi-agent flowshop scheduling with release dates","authors":"Danyu Bai , Wenjia Zheng , Chenbo Zang , Jie Yang , Chin-Chia Wu , Hu Qin","doi":"10.1016/j.swevo.2025.102101","DOIUrl":"10.1016/j.swevo.2025.102101","url":null,"abstract":"<div><div>The globalization of production has accelerated the growth of contract manufacturing, as brand firms increasingly outsource production to specialized manufacturers to reduce costs and improve efficiency. To meet rising production demands, contract manufacturers establish production facilities across global regions, leveraging localized advantages in labor costs, raw material access, and logistics infrastructure. Contract manufacturers in distributed assembly-line systems face the critical challenge of dynamically coordinating order allocation across decentralized facilities to satisfy multi-client requirements. This study introduces a distributed bi-agent permutation flowshop scheduling for minimizing the makespans of both agents while considering release dates to simulate real-world production scenarios. An exact branch-and-bound algorithm is proposed for optimizing the weighted sum of two objectives. A novel Q-learning-based artificial bee colony algorithm is presented to construct high-quality Pareto frontiers for the bi-objective optimization problem. The effectiveness of the proposed algorithms is validated through a comprehensive set of numerical experiments.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102101"},"PeriodicalIF":8.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913058","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

Differential evolution algorithm with local and global parameter adaptation 具有局部和全局参数自适应的差分进化算法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-28 DOI: 10.1016/j.swevo.2025.102125

Xiao Lin Jin , Sheng Xin Zhang , Li Ming Zheng , Shao Yong Zheng

{"title":"Differential evolution algorithm with local and global parameter adaptation","authors":"Xiao Lin Jin , Sheng Xin Zhang , Li Ming Zheng , Shao Yong Zheng","doi":"10.1016/j.swevo.2025.102125","DOIUrl":"10.1016/j.swevo.2025.102125","url":null,"abstract":"<div><div>Differential Evolution (DE) is an effective meta-heuristic algorithm for numerical optimization. However, it suffers from persistent limitations such as sensitivity to parameter settings and premature convergence tendencies. This paper presents a novel Local and Global Parameter Adaptation (LGP) mechanism to mitigate these deficiencies through two key innovations. First, we develop a dual historical memory strategy that dynamically classifies successful control parameters into local or global historical record based on the Euclidean distance between parent-offspring vector pairs, the local and global historical memory are updated accordingly at each generation. Second, we introduce a parameter adaptation strategy that adaptively selects elements from appropriate historical memory for the generation of new control parameters to maintain exploitation-exploration balance. Extensive experimental validation demonstrates LGP’s effectiveness. When integrated with four DE variants, LGP consistently improves their performance, and the LGP-enhanced algorithm demonstrates remarkable performance compared with seven State-of-the-Art DE algorithms. Results confirm that LGP improves solution accuracy and prevents premature convergence simultaneously.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102125"},"PeriodicalIF":8.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913057","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

Metaheuristic-optimized TabNet ensemble for accurate and interpretable obesity classification meta启发式优化的TabNet集成，用于准确和可解释的肥胖分类

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-28 DOI: 10.1016/j.swevo.2025.102128

Zarindokht Helforoush, Mitra Shojaie, Sahel Arghamiri

{"title":"Metaheuristic-optimized TabNet ensemble for accurate and interpretable obesity classification","authors":"Zarindokht Helforoush, Mitra Shojaie, Sahel Arghamiri","doi":"10.1016/j.swevo.2025.102128","DOIUrl":"10.1016/j.swevo.2025.102128","url":null,"abstract":"<div><div>Obesity is a complex global health issue with severe implications for both individual well-being and public health systems. It has been traditionally challenging to predict and diagnose due to its multifactorial nature, involving genetic, behavioral, and environmental factors. While classical regression models have been extensively used for obesity prediction, their limitations have prompted the exploration of more advanced methodologies. In this study, we leverage Deep Learning (DL) techniques, particularly TabNet, to address the challenges of obesity classification in tabular data—a domain where DL’s potential has often been underutilized. Our approach enhances the TabNet architecture through effective hyperparameter tuning, utilizing Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Hunger Games Search (HGS). The resulting models, TabNet-PSO, TabNet-GWO, and TabNet-HGS are combined into a novel ensemble that demonstrates superior performance in obesity classification compared to conventional machine-learning models and recent studies. Additionally, Explainable Artificial Intelligence techniques are employed to provide both local and global interpretability of model predictions, using SHapley Additive exPlanations (SHAP). This interpretability is crucial in clinical settings, where understanding the underlying factors influencing predictions is essential. The study’s findings offer significant contributions to the early detection and management of obesity, providing healthcare professionals with precise and interpretable predictions to guide intervention strategies.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102128"},"PeriodicalIF":8.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913056","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

Incremental learning-enhanced ensemble surrogate-assisted evolutionary algorithm for lifelong berth allocation and quay crane assignment problems 基于增量学习增强的集成代理辅助进化算法的终身泊位分配和码头起重机分配问题

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-28 DOI: 10.1016/j.swevo.2025.102133

Li-Sha Xu , Yi-Ming Wang , Ting Huang , Yue-Jiao Gong , Jing Liu

{"title":"Incremental learning-enhanced ensemble surrogate-assisted evolutionary algorithm for lifelong berth allocation and quay crane assignment problems","authors":"Li-Sha Xu , Yi-Ming Wang , Ting Huang , Yue-Jiao Gong , Jing Liu","doi":"10.1016/j.swevo.2025.102133","DOIUrl":"10.1016/j.swevo.2025.102133","url":null,"abstract":"<div><div>The berth allocation and quay crane assignment problem (BACAP) is a critical challenge in maritime transport, especially in lifelong scenarios that are rarely addressed in the current literature but essential for practical applications. The Lifelong BACAP (LBACAP) presents new challenges, such as the uncertain arrival of vessels, limited resources, and inter-dependencies between vessels. To address these challenges, we propose an incremental learning-enhanced ensemble surrogate-assisted evolutionary algorithm, named IL-ESAEA, with three core designs. (1) The adaptive rolling-horizon strategy divides the LBACAP into consecutive time windows, each corresponding to an interconnected sub-LBACAP. (2) The ensemble surrogate-assisted evolutionary algorithm (ESAEA) approximates the computationally intensive and intricately designed decoding method for optimization, reducing computational costs while maintaining robust search capabilities for solving various BACAPs. (3) The incremental learning mechanism identifies connections between sub-LBACAPs in successive time windows, utilizing historical decisions to guide the optimization effectively. Experimental results demonstrate that IL-ESAEA consistently outperforms state-of-the-art algorithms and provides superior solutions with increased computational efficiency over time. This highlights the strong competitive edge of IL-ESAEA in solving LBACAPs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102133"},"PeriodicalIF":8.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913055","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

Multi-pass planning for multi-vehicle cooperative urban demining: A knowledge-driven evolutionary approach with RL-enhanced neighborhood search 多车辆协同城市排雷的多通道规划：基于强化学习的社区搜索的知识驱动进化方法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-26 DOI: 10.1016/j.swevo.2025.102129

Hao Cheng , Jin Yi , Huayan Pu , Jun Luo , Chao Lu

{"title":"Multi-pass planning for multi-vehicle cooperative urban demining: A knowledge-driven evolutionary approach with RL-enhanced neighborhood search","authors":"Hao Cheng , Jin Yi , Huayan Pu , Jun Luo , Chao Lu","doi":"10.1016/j.swevo.2025.102129","DOIUrl":"10.1016/j.swevo.2025.102129","url":null,"abstract":"<div><div>It has become increasingly urgent and necessary to coordinate multiple unmanned systems to efficiently execute a variety of complex tasks in place of humans. This paper focus on the problem of multi-vehicle demining in urban road networks (MVDMP). First, a mixed-integer programming model is established, taking into account both the topological connectivity of the road network and the demining width of the vehicles. Second, an evolutionary learning algorithm incorporating Q-learning (QEA) is proposed to effectively solve this problem. In the initialization phase, a hybrid initialization strategy, which includes two heuristic rules, is introduced to generate high-quality initial solutions. During the local search phase, six neighborhood search operators are proposed based on problem characteristics, and Q-learning is used to adaptively customize perturbation schemes for individuals. Additionally, the Metropolis acceptance criterion is employed to balance exploration and exploitation. Finally, extensive experiments on instances of varying sizes derived from urban road networks (Sioux Falls, Sydney, etc.) demonstrate the efficiency and superiority of the proposed method compared to other four state-of-the-art approaches.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102129"},"PeriodicalIF":8.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902325","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 comprehensive survey of adaptive strategies in differential evolutionary algorithms 差分进化算法中自适应策略的综合研究

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-25 DOI: 10.1016/j.swevo.2025.102081

Xinggui Ye , Jianping Li , Peng Wang , Ponnuthurai Nagaratnam Suganthan

{"title":"A comprehensive survey of adaptive strategies in differential evolutionary algorithms","authors":"Xinggui Ye , Jianping Li , Peng Wang , Ponnuthurai Nagaratnam Suganthan","doi":"10.1016/j.swevo.2025.102081","DOIUrl":"10.1016/j.swevo.2025.102081","url":null,"abstract":"<div><div>Classical differential evolution (DE) encounters premature convergence when dealing with diverse optimization problems. This challenge has encouraged extensive research efforts aimed at improving and enhancing the original methodologies. Among the various improvement techniques, adaptive strategies have been universally employed. However, there is a lack of systematic research on the adaptation mechanisms. This work comprehensively investigates the adaptive strategies adopted in DE algorithms. Typical adaptation strategies employed in DE algorithms are refined and summarized, highlighting their characteristics. A new taxonomy of adaptation strategies is proposed, categorizing them based on their primary properties, which include adaptations of control parameters, mutation strategies, population size, search space, learning schemes, and composite adaptations. The advantages and disadvantages of these adaptation strategies are summarized, elucidating their unique characteristics. Additionally, a general framework with an adaptive updating engine is proposed, which can serve as a reference for developing new DE algorithms or improving existing ones. The paper also highlights the challenges and open issues of adaptive strategies, suggesting several promising research directions.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102081"},"PeriodicalIF":8.5,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893288","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