Swarm and Evolutionary Computation最新文献_第7页

A decomposition framework with dual populations and dual stages for constrained multi-objective optimization 约束多目标优化的双种群双阶段分解框架

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-20 DOI: 10.1016/j.swevo.2025.102030

Zhanglu Hou , Jialu Ye , Yizhang Xia , Yibin Gong , Juan Zou , Shengxiang Yang

{"title":"A decomposition framework with dual populations and dual stages for constrained multi-objective optimization","authors":"Zhanglu Hou , Jialu Ye , Yizhang Xia , Yibin Gong , Juan Zou , Shengxiang Yang","doi":"10.1016/j.swevo.2025.102030","DOIUrl":"10.1016/j.swevo.2025.102030","url":null,"abstract":"<div><div>The main challenge of constrained multi-objective optimization involves achieving a good balance among feasibility, convergence, and diversity simultaneously. However, most existing methods exhibit an imbalance, making it hard to converge towards Pareto-optimal front (PF) while maintaining the diversity of feasible solutions. To address this issue, this paper proposes a new dual-population and dual-stage constrained multi-objective optimization algorithm, denoted as DD-M2M, based on the multi-objective-to-multi-objective decomposition framework (M2M). In this approach, dual populations, referred to as <span><math><mrow><mi>C</mi><mspace></mspace><mi>P</mi></mrow></math></span> and <span><math><mrow><mi>D</mi><mspace></mspace><mi>P</mi></mrow></math></span>, are employed collaboratively. More specifically, in the first stage, the <span><math><mrow><mi>D</mi><mspace></mspace><mi>P</mi></mrow></math></span> evolves independently without considering constraints, focusing solely on convergence towards the unconstrained PF, while the <span><math><mrow><mi>C</mi><mspace></mspace><mi>P</mi></mrow></math></span> evolves with a weak collaboration with the <span><math><mrow><mi>D</mi><mspace></mspace><mi>P</mi></mrow></math></span>, driven by feasibility-based environment selection rules. In the second stage, the dual populations are both divided into multiple sub-populations within the M2M framework, generating offspring in a strongly collaborative manner to eventually converge to the constrained PF while ensuring solution diversity. Experimental results on two widely-used test suites fully demonstrate the superiority of DD-M2M compared to seven state-of-the-art methods on most test problems. Additionally, the proposed method is applied to real-world problems, and experimental results confirm its effectiveness in addressing practical challenges.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102030"},"PeriodicalIF":8.2,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322625","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

Balancing objective and switch cost for robust optimization over time 平衡目标和切换成本的鲁棒优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-20 DOI: 10.1016/j.swevo.2025.102022

Zhening Liu, Handing Wang

{"title":"Balancing objective and switch cost for robust optimization over time","authors":"Zhening Liu, Handing Wang","doi":"10.1016/j.swevo.2025.102022","DOIUrl":"10.1016/j.swevo.2025.102022","url":null,"abstract":"<div><div>Dynamic optimization problems (DOPs) commonly arise in real-world scenarios where objective functions and constraints change over time. While existing methods primarily focus on tracking optimal solutions across different environments, the cost of switching solutions cannot be overlooked in practical applications. Robust optimization over time (ROOT) addresses this challenge by simultaneously maximizing objective values and minimizing solution switch costs. This paper proposes a novel ROOT method, introducing a dynamic balancing mechanism that adjusts the search direction according to the correlation between objective value and switch cost. Additionally, a robust solution selection strategy is developed, utilizing switch cost as a constraint to pre-screen solutions before selecting the optimal robust solution. Comprehensive experiments on ROOT test problems of varying dimensions and switch cost weights validate the effectiveness of the proposed approach. Comparisons with ROOT algorithms, dynamic evolutionary algorithms, multi-objective evolutionary algorithms and single-objective evolutionary algorithms demonstrate that the proposed method achieves superior overall performance. Furthermore, ablation studies confirm the effectiveness of the balancing mechanism and the robust solution selection strategy in enhancing optimization quality.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102022"},"PeriodicalIF":8.2,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331058","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 co-evolutionary algorithm for dynamic large-scale crude oil scheduling 基于知识转移的大规模原油动态调度协同进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-19 DOI: 10.1016/j.swevo.2025.102024

Wanting Zhang, Wenli Du, Wei Du

{"title":"A knowledge transfer-based co-evolutionary algorithm for dynamic large-scale crude oil scheduling","authors":"Wanting Zhang, Wenli Du, Wei Du","doi":"10.1016/j.swevo.2025.102024","DOIUrl":"10.1016/j.swevo.2025.102024","url":null,"abstract":"<div><div>Confronted with the intricacies arising from expanding production scales and numerous uncertain events, large-scale dynamic scheduling has emerged as a practical approach in industries. However, within the framework of predictive–reactive scheduling, few works have explored the trade-off between the optimality and stability of the solutions generated. To fill this gap, this paper investigates dynamic scheduling applied to the large-scale crude oil scheduling problem, which is critical for the petroleum industry. Specifically, we develop a model that incorporates multiple sources of uncertainty: vessel arrival delays; tank malfunctions; fluctuations in feed flowrates to the distillation columns; and intermediate product demand. To solve this problem effectively, a knowledge transfer-based cooperative co-evolutionary algorithm (KT-CCEA) is proposed, where specific knowledge from the predictive stage is transferred to the reactive stage. Specifically, multiple subpopulations are generated around distinct reactive points, evolving across diverse search dimensions, to balance optimality and stability. Discretized differential operators are designed to overcome the limitations of standard evolutionary operators in integer-coded matrix representation. Empirical results over a set of 15 benchmark instances validate the superiority of the proposed KT-CCEA over four state-of-the-art algorithms (RSCO-SAGA, VLCEA, DMDE, and RCI-PSO). Ablation experiments on seven algorithm variants further confirm the efficacy of its core components.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102024"},"PeriodicalIF":8.2,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313795","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

LABCAT: Locally adaptive Bayesian optimization using principal-component-aligned trust regions LABCAT：使用主成分对齐信任区域的局部自适应贝叶斯优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-16 DOI: 10.1016/j.swevo.2025.101986

E. Visser, C.E. van Daalen, J.C. Schoeman

引用次数: 0

Expensive constrained multi-objective optimization via adaptive surrogate-assisted dense weight multi-objective evolutionary algorithm 基于自适应代理辅助密集加权多目标进化算法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 : 2025-06-16 DOI: 10.1016/j.swevo.2025.102033

Jiansheng Liu , Haoran Hu , Zhiyong Liu , Zan Yang , Liming Chen , Xiwen Cai

{"title":"Expensive constrained multi-objective optimization via adaptive surrogate-assisted dense weight multi-objective evolutionary algorithm","authors":"Jiansheng Liu , Haoran Hu , Zhiyong Liu , Zan Yang , Liming Chen , Xiwen Cai","doi":"10.1016/j.swevo.2025.102033","DOIUrl":"10.1016/j.swevo.2025.102033","url":null,"abstract":"<div><div>Expensive constrained multi-objective optimization problems (ECMOPs) face challenges in obtaining excellent results for complex <em>PF</em> shapes within limited costly evaluations efficiently and balancing the optimizing on constraints and objectives. Also, one surrogate typically cannot provide the consistent predictive abilities for multiple objectives or constraints with diverse features. This paper designs an adaptive surrogate-assisted dense weight multi-objective evolutionary algorithm (ASDWMOEA), where efficient dense weight-based dual-population evolution and effective surrogate switch mechanism are integrated. Specifically, when there is no feasible solution in the population, the algorithm ignores the constraints of the problem and uses Kriging surrogate model to optimize only for the objective of the problem. When the population enters the feasible domain, the algorithm uses the association information of external weights to implement three mutation operations for generating a large set of high-quality candidate solutions. Each external weight is then refined to produce multiple internal weights, and an elite subset of the candidate solutions is selected to form the internal population corresponding to these internal weights. Subsequently, sequential global and local searches are conducted on the internal population, and the elite individual with the most significant improvement is selected for each internal weight. Hence, the external population is updated based the two metric-based selection strategy, and the algorithm adaptively switches between the employed Kriging and RBF surrogate models based on updates to the external population. Finally, the algorithm is evaluated against seventeen advanced and latest algorithms using five test suites. The results demonstrate that ASDWMOEA exhibits strong performance.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102033"},"PeriodicalIF":8.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291274","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 offline-online learning framework combining meta-learning and reinforcement learning for evolutionary multi-objective optimization 结合元学习和强化学习的离线-在线学习框架，用于进化多目标优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-14 DOI: 10.1016/j.swevo.2025.102037

Shuxiang Li , Yongsheng Pang , Zhaorong Huang , Xianghua Chu

{"title":"An offline-online learning framework combining meta-learning and reinforcement learning for evolutionary multi-objective optimization","authors":"Shuxiang Li , Yongsheng Pang , Zhaorong Huang , Xianghua Chu","doi":"10.1016/j.swevo.2025.102037","DOIUrl":"10.1016/j.swevo.2025.102037","url":null,"abstract":"<div><div>Many multi-objective evolutionary algorithms (MOEAs) have been proposed in addressing the multi-objective optimization problems (MOPs). However, the performance of MOEAs varies significantly across various MOPs and there is no single MOEA that performs well on all MOP instances. In addition, existing methods for adaptive MOEA selection still face limitations, which restrict the further optimization for MOPs. To fill these gaps and improve the efficiency of solving MOPs, this study proposes an offline-online learning framework combining meta-learning and reinforcement learning (O<sup>2</sup>-MRL). Instead of proposing a new MOEA or optimizing a strategy, O<sup>2</sup>-MRL solves MOPs by taking full advantage of the existing MOEAs and addresses the limitations of existing MOEA selection methods. O<sup>2</sup>-MRL can adaptively select the appropriate MOEAs for various types of MOPs with different dimensions (Offline) and automatically schedule the selected MOEAs during the optimization process (Online), offering a new idea for optimizing MOPs. To evaluate the performance of the proposed O<sup>2</sup>-MRL, forty-seven benchmark MOPs are used as instances, and nine representative MOEAs are selected for comparison. Comprehensive experiments demonstrate the significant efficiency of O<sup>2</sup>-MRL, as it achieves optimal solutions in 60.28 % of the MOPs across different dimensions and improves the optimization results in 48.23 % of them, with an average improvement of 8.72 %. In addition to maintaining high optimization performance, O<sup>2</sup>-MRL also demonstrates superior convergence speed and stability across various types of MOPs. Two real-world MOPs are employed to evaluate the practicality of O<sup>2</sup>-MRL, and the experimental results indicate that it achieves optimal solutions in both cases.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102037"},"PeriodicalIF":8.2,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279770","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-attention-powered learning genetic algorithm for real-world 3D wind farm layout optimization 多注意力驱动学习遗传算法用于现实世界三维风电场布局优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-14 DOI: 10.1016/j.swevo.2025.102018

Jiaru Yang , Yaotong Song , Weiping Ding , Jun Tang , Zhenyu Lei , Shangce Gao

{"title":"Multi-attention-powered learning genetic algorithm for real-world 3D wind farm layout optimization","authors":"Jiaru Yang , Yaotong Song , Weiping Ding , Jun Tang , Zhenyu Lei , Shangce Gao","doi":"10.1016/j.swevo.2025.102018","DOIUrl":"10.1016/j.swevo.2025.102018","url":null,"abstract":"<div><div>Wind farm layout optimization plays a crucial role in improving wind energy utilization, reducing construction and operational costs, enhancing the reliability and stability of wind farms, and promoting technological innovation in wind energy. However, this NP-hard problem is often approached in current research under idealized conditions, typically assuming a flat plane with no consideration of elevation. To address these limitations, we propose a 3D wind farm optimization layout framework that incorporates a 3D Gaussian wake model, accounting for spatial factors like terrain elevation to more closely reflect real-world engineering conditions. To handle the high-dimensional complexity of 3D wind farm layout optimization, we introduce a multi-head attention-based genetic learning algorithm, named ALGA, that learns and leverages successful evolutionary patterns within the population. This enables the calculation of attention scores for promising regions in the search space. By iteratively refining high-scoring regions, the population achieves greater vitality and has a stronger ability to escape local optima, optimizing continuously toward the best solutions while maximizing energy conversion efficiency and minimizing wake effects. Our study involves two cases: one with ideal terrain and four standard wind speeds, and another that simulates the real terrain and annual wind conditions of the Guishan wind farm project. Across total 24 experimental scenarios, ALGA achieves the highest energy conversion efficiency, outperforming seven other state-of-the-art algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102018"},"PeriodicalIF":8.2,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289155","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 dual model-based evolutionary framework for dynamic large-scale sparse multiobjective optimization 基于双模型的动态大规模稀疏多目标优化进化框架

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-13 DOI: 10.1016/j.swevo.2025.102011

Panpan Zhang , Ru Zhang , Ye Tian , Kay Chen Tan , Xingyi Zhang

{"title":"A dual model-based evolutionary framework for dynamic large-scale sparse multiobjective optimization","authors":"Panpan Zhang , Ru Zhang , Ye Tian , Kay Chen Tan , Xingyi Zhang","doi":"10.1016/j.swevo.2025.102011","DOIUrl":"10.1016/j.swevo.2025.102011","url":null,"abstract":"<div><div>Recently, there has been a growing interest in dynamic multiobjective optimization problems (DMOPs). Although some evolutionary algorithms have been tailored for DMOPs, their effectiveness is limited when handling large-scale DMOPs, especially those characterized by sparsity, where most variables in Pareto-optimal solutions are equal to zero. To address this issue, this paper proposes a dual model-based evolutionary framework to solve dynamic large-scale sparse multiobjective optimization problems (DSMOPs). Specifically, the proposed framework addresses dynamic changes by predicting a new initial population for a static multiobjective optimization evolutionary algorithm in the new environment. Based on the idea of initial population prediction, the proposed framework transforms the large-scale variable prediction into the small-scale variable prediction, where support vector regression is introduced to predict the sparse distributions of the new initial population to reduce the decision space, and multilayer perceptron is performed on the reduced space to predict its continuous distributions. By integrating the two simplified predictions, a two-layer change response mechanism is constructed to ensure both the sparsity and quality of the obtained solutions. In addition, this paper designs the benchmark and real-world test problems to assess the performance of the proposed framework for tackling large-scale DMOPs. Experimental results demonstrate the superiority of the proposed framework compared with the six state-of-the-art algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102011"},"PeriodicalIF":8.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271114","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

Surrogate-Assisted Differential Evolution Light Gradient Boosting Machine for Mangrove Aboveground Biomass inversion 红树林地上生物量反演的代理辅助差分进化光梯度增强机

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-13 DOI: 10.1016/j.swevo.2025.102013

Yangdi Shen , Zuowen Liao , Yichao Tian , Jin Tao , JinXuan Luo , Jiale Wang , Qiang Zhang

{"title":"Surrogate-Assisted Differential Evolution Light Gradient Boosting Machine for Mangrove Aboveground Biomass inversion","authors":"Yangdi Shen , Zuowen Liao , Yichao Tian , Jin Tao , JinXuan Luo , Jiale Wang , Qiang Zhang","doi":"10.1016/j.swevo.2025.102013","DOIUrl":"10.1016/j.swevo.2025.102013","url":null,"abstract":"<div><div>Mangrove Aboveground Biomass (AGB) inversion holds considerable importance in safeguarding and rehabilitating blue carbon ecosystems, as well as their ability to adapt to climate change. In recent years, machine learning models based on heuristic algorithms for solving mangrove AGB inversion problems have gained wildly attention. However, these hybrid models are facing challenges, including expensive computation costs and low convergence speeds. Thus, an efficient Surrogate-Assisted Differential Evolution Light Gradient Boosting Machine algorithm (SADE-LGBM) is proposed to estimate mangrove AGB in the Maowei Sea, Beibu Gulf of China. This algorithm mainly contains: (i) Introducing a radial basis function surrogate model to predict the virtual fitness and identify promising solutions. (ii) An updating population strategy is proposed to update promising solutions to population efficiently. (iii) DE algorithm and LGBM model are combined to address hyperparameter optimization and feature selection simultaneously. To evaluate the performance of SADE-LGBM, we used a dataset consisting of 227 quadrat data collected from field surveys and compared SADE-LGBM with fourteen other algorithms. The experimental results illustrate that SADE-LGBM achieves the best metrics of <span><math><msup><mrow><mtext>R</mtext></mrow><mrow><mn>2</mn></mrow></msup></math></span> = 0.8351, RMSE <span><math><mo>=</mo></math></span> 220.4698, with the predicted range of mangrove AGB being 3.2765–207.5331 Mg/ha. The SADE-LGBM algorithm demonstrates its potential as a reliable algorithm for estimating large-scale mangrove AGB.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102013"},"PeriodicalIF":8.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271116","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-agent reinforcement learning-aided evolutionary algorithm for a many-objective distributed hybrid flow shop scheduling problem 多智能体强化学习辅助进化算法求解多目标分布式混合流水车间调度问题

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-13 DOI: 10.1016/j.swevo.2025.101991

Binhui Wang , Hongfeng Wang , Qi Yan , Enjie Ma

{"title":"Multi-agent reinforcement learning-aided evolutionary algorithm for a many-objective distributed hybrid flow shop scheduling problem","authors":"Binhui Wang , Hongfeng Wang , Qi Yan , Enjie Ma","doi":"10.1016/j.swevo.2025.101991","DOIUrl":"10.1016/j.swevo.2025.101991","url":null,"abstract":"<div><div>The distributed hybrid flow shop scheduling problem (DHFSP) is common in real-world production environments and is typically constrained by factors such as time-of-use electricity tariffs, due dates, and worker assignments. The complexity of these factors often requires decision-makers to consider multiple optimization objectives simultaneously. Consequently, this paper investigates a many-objective DHFSP and establishes the corresponding mathematical model. To efficiently solve the model, a multi-agent reinforcement learning-aided evolutionary algorithm (MRLEA) is developed. In MRLEA, a grid-based evolutionary framework is introduced to enhance the selection pressure of non-dominated solutions while maintaining their diversity. Meanwhile, an intelligent local search process based on multi-agent group decision-making is integrated into the evolutionary framework to improve convergence speed and overcome local optima. Specifically, each agent corresponds to an optimization objective and must engage in collaborative reinforcement learning with other agents to adaptively assign local search strategies for each solution. After executing the local search, the rewards for agents that make crucial contributions to the final decision are updated using the state-of-the-art Reward Centering technique. Additionally, a dominance judgment is made between the old and new solutions in the corresponding objective dimensions of these agents to retain the more promising solutions. Comprehensive experiments are conducted to validate the superior performance of MRLEA in addressing the proposed many-objective DHFSP.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 101991"},"PeriodicalIF":8.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271117","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