Swarm and Evolutionary Computation最新文献_第6页

A heterogeneous sparsity knowledge guided evolutionary algorithm for sparse large-scale multiobjective optimization 基于异构稀疏知识的大规模稀疏多目标优化进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-03 DOI: 10.1016/j.swevo.2025.102000

Jing Jiang , Huoyuan Wang , Pingping Tong , Juanjuan Hong , Zhe Liu , Xing Zhuang , Benyue Su , Fei Han

{"title":"A heterogeneous sparsity knowledge guided evolutionary algorithm for sparse large-scale multiobjective optimization","authors":"Jing Jiang , Huoyuan Wang , Pingping Tong , Juanjuan Hong , Zhe Liu , Xing Zhuang , Benyue Su , Fei Han","doi":"10.1016/j.swevo.2025.102000","DOIUrl":"10.1016/j.swevo.2025.102000","url":null,"abstract":"<div><div>Research on sparse large-scale multiobjective optimization problems (LSMOPs) is rapidly growing due to their diverse applications in science and engineering. Existing studies typically employ static or dynamic knowledge to guide the search in evolutionary algorithms for solving sparse LSMOPs. However, relying solely on a single type of sparsity knowledge may result in ambiguous guidance and suboptimal optimization. To address this, we propose an evolutionary algorithm based on heterogeneous sparsity knowledge (HSKEA). In this approach, static knowledge is represented by a scoring vector to assess the importance of each decision variable, while dynamic knowledge is captured by indicator vectors that identify whether a decision variable is zero or non-zero and iteratively updates based on the population distribution. Two types of populations, including main and auxiliary populations, are initialized using both dynamic and static knowledge and evolved through a new genetic operator guided by heterogeneous sparsity knowledge and information sharing. Experimental results comparing HSKEA to four state-of-the-art algorithms across eight benchmark test problems and three real-world scenarios demonstrate its effectiveness.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 102000"},"PeriodicalIF":8.2,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203749","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

Diversity-enhanced hyper-heuristics for multi-objective dynamic flexible job shop scheduling 多目标动态柔性作业车间调度的多样性增强超启发式算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-02 DOI: 10.1016/j.swevo.2025.101994

Yuan Shi , Yaoming Yang , Bingdong Li , Hong Qian , Hao Hao , Aimin Zhou

引用次数: 0

Enhancing automatically designed relocation rules with the rollout algorithm 利用rollout算法增强自动设计的重新定位规则

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-31 DOI: 10.1016/j.swevo.2025.101975

Marko Đurasević , Mateja Đumić , Francisco Javier Gil Gala

{"title":"Enhancing automatically designed relocation rules with the rollout algorithm","authors":"Marko Đurasević , Mateja Đumić , Francisco Javier Gil Gala","doi":"10.1016/j.swevo.2025.101975","DOIUrl":"10.1016/j.swevo.2025.101975","url":null,"abstract":"<div><div>The container relocation problem (CRP) is a complex optimisation problem in maritime transport. To solve this problem, heuristic approaches are often used, ranging from relocation rules (RRs) to metaheuristics. Although metaheuristics outperform RRs, the latter remain popular due to their simplicity and adaptability. The manual design of RRs is challenging, which is why genetic programming (GP) is used to automatically generate them. However, RRs generated by GP generally achieved inferior solutions compared to metaheuristics. To close this gap, this study applies the rollout method to improve the performance of RRs while maintaining reasonable execution times. The rollout algorithm strikes a balance between exhaustive and heuristic search by combining partial enumeration with RR-based decision evaluation. Although the rollout method improves the quality of the solution, it also leads to considerable computational cost. To solve this problem, three strategies for reducing the search space are proposed. Experimental results show that the rollout algorithm significantly improves solution quality compared to standard RRs, with the proposed search space reduction techniques effectively reducing execution time without compromising performance. In particular, the results show that the rollout algorithm can be executed 2 to 4 times faster using the proposed reduction techniques, while its performance is reduced only by 1%.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101975"},"PeriodicalIF":8.2,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184539","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

Archive assisted fully informed evolutionary algorithm for expensive many-objective optimization 基于档案辅助的全信息进化算法的昂贵多目标优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-30 DOI: 10.1016/j.swevo.2025.101988

Jie Lin , Sheng Xin Zhang , Shao Yong Zheng , Kwai Man Luk

{"title":"Archive assisted fully informed evolutionary algorithm for expensive many-objective optimization","authors":"Jie Lin , Sheng Xin Zhang , Shao Yong Zheng , Kwai Man Luk","doi":"10.1016/j.swevo.2025.101988","DOIUrl":"10.1016/j.swevo.2025.101988","url":null,"abstract":"<div><div>In many real-world engineering and scientific optimization scenarios, practitioners often face expensive many-objective optimization problems where evaluating candidate solutions incurs prohibitive computational costs. The inherent scarcity of truly calculated data often leads to the construction of models with high uncertainty using limited datasets. This uncertainty can adversely affect the Surrogate-assisted Many-Objective Evolutionary Algorithms (SAMaOEAs). To address this issue and enhance performance, this paper introduces an Archive-assisted Fully Informed Evolutionary Algorithm (AFIEA). In AFIEA, two kinds of models are constructed from archive data to simultaneously predict objective values and uncertainty trends (whether the predictions are overestimated or underestimated). With this foundation, both the optimizer and infill criterion processes are fully guided by the predicted objective values and uncertainty trends. In the optimization phase, a novel Uncertainty Trend Classification (UTC)-based Upper Confidence Bound is employed as the acquisition function. During the infill criterion phase, UTC is used to preprocess the population, enhancing the selection probability of under-estimated solutions, while an archive-based metric selects more precise solutions, guided by the archive in terms of convergence and diversity. The performance of AFIEA is compared with six state-of-the-art SAMaOEAs on artificial benchmark problems and one real-world expensive optimization problem within a limited budget. In the benchmark tests, AFIEA outperforms the six advanced SAMaOEAs across most of the test functions, demonstrating that the proposed mechanism offers strong generality and enhanced search performance. Additionally, in the optimization of electromagnetic devices, AFIEA achieves superior population quality in a shorter time with a limited number of simulations.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101988"},"PeriodicalIF":8.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167262","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 hybrid surrogate-assisted dual-population co-evolutionary algorithm for multi-area integrated scheduling in wafer fabs 晶圆厂多区域集成调度的混合代理辅助双种群协同进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-29 DOI: 10.1016/j.swevo.2025.102016

Mengyu Jin , Peng Zhang , Youlong Lv , Ming Wang , Wenbing Xiang , Hongsen Li , Jie Zhang

{"title":"A hybrid surrogate-assisted dual-population co-evolutionary algorithm for multi-area integrated scheduling in wafer fabs","authors":"Mengyu Jin , Peng Zhang , Youlong Lv , Ming Wang , Wenbing Xiang , Hongsen Li , Jie Zhang","doi":"10.1016/j.swevo.2025.102016","DOIUrl":"10.1016/j.swevo.2025.102016","url":null,"abstract":"<div><div>In wafer fabrication, multiple areas handle different processes and production flows. To maintain the desired chemical and physical properties of wafers, strict time window constraints (TWCs) must be observed as wafers progress through these areas. However, independent scheduling within each area without collaboration complicates resource allocation and hinders overall production optimization. Implementing multi-area integrated scheduling is thus essential for effective production management, aiming to reduce total lead time and production costs. This paper proposes a hybrid surrogate-assisted dual-population co-evolutionary algorithm (HSA-DPEA) to efficiently tackle the multi-area integrated scheduling problem under multiple TWCs. The algorithm employs a dual-population co-evolutionary mechanism, consisting of normal and auxiliary populations, to balance convergence and diversity while ensuring feasibility. The normal population focuses on feasible solutions to maintain overall quality, while the auxiliary population explores infeasible regions to identify promising individuals that can guide the normal population's evolution. To enhance evolutionary efficiency and reduce the number of time-consuming real fitness evaluations, a hybrid surrogate-assisted model is introduced. This model adapts by training regression or classification models at different stages of population evolution. Additionally, an online learning strategy based on convergence and diversity is employed for continuous model updating to improve accuracy. The proposed algorithm is tested on 18 instances and validated through six months of continuous testing on a wafer fab simulation system. The results demonstrate that HSA-DPEA obtains better Pareto optimal sets, effectively reducing total lead time and production costs in multi-area integrated scheduling under multiple TWCs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 102016"},"PeriodicalIF":8.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167260","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 balance-oriented iterative greedy algorithm for the distributed heterogeneous hybrid flow-shop scheduling problem with blocking constraints 针对具有阻塞约束的分布式异构混合流车间调度问题，提出了一种面向平衡的迭代贪心算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-29 DOI: 10.1016/j.swevo.2025.102015

Xiuli Wu, Yang Zhao

{"title":"A balance-oriented iterative greedy algorithm for the distributed heterogeneous hybrid flow-shop scheduling problem with blocking constraints","authors":"Xiuli Wu, Yang Zhao","doi":"10.1016/j.swevo.2025.102015","DOIUrl":"10.1016/j.swevo.2025.102015","url":null,"abstract":"<div><div>With the globalization of economy, production tasks usually need to be allocated among multiple factories to achieve a more efficient delivery. This paper studies the distributed heterogeneous hybrid flow-shop scheduling problem with blocking constraints (DHHFSPB) and proposes a balance-oriented iterative greedy algorithm(BOIG). The sigmoid-based adaptive(SA) decoding method is proposed to dynamically explore the solution space. Considering the characteristics of the problem, four initialization methods are developed to generate the initial solutions. Various operators are presented to balance the loads among factories. Some production tasks in the high-load factories are reassigned to the low-load factories by the perturbation operator. The structure of the solution is reorganized by the destruction and construction operators in a load-oriented manner. The local search operator balances the exploration and exploitation and a new neighborhood structure for the distributed problem is proposed. Additionally, an improved metropolis criterion is adopted to accept solutions. The results of experiments show that the BOIG algorithm can effectively solve the DHHFSPB.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 102015"},"PeriodicalIF":8.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167261","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

Swarm intelligence applications for emergency evacuation planning: state of the art, recent developments, and future research opportunities 群体智能在紧急疏散计划中的应用：现状、最新发展和未来的研究机会

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-29 DOI: 10.1016/j.swevo.2025.102009

Razieh Khayamim , Ren Moses , Eren E. Ozguven , Marta Borowska-Stefańska , Szymon Wiśniewski , Maxim A. Dulebenets

{"title":"Swarm intelligence applications for emergency evacuation planning: state of the art, recent developments, and future research opportunities","authors":"Razieh Khayamim , Ren Moses , Eren E. Ozguven , Marta Borowska-Stefańska , Szymon Wiśniewski , Maxim A. Dulebenets","doi":"10.1016/j.swevo.2025.102009","DOIUrl":"10.1016/j.swevo.2025.102009","url":null,"abstract":"<div><div>In the era where natural and human-made disasters are escalating in frequency and impact, the need for advanced emergency evacuation strategies is more critical than ever. This study presents a comprehensive examination of swarm intelligence algorithms and their applications in emergency evacuation planning—a field that has become increasingly important due to the growing complexity and scale of evacuation challenges. We delve into the realm of swarm intelligence—a class of algorithms inspired by self-organized behaviors observed in nature, such as those in ant colonies, bee colonies, bird flocks, and fish schools. Focusing on specific algorithms, including particle swarm optimization (PSO), artificial bee colony (ABC), and ant colony optimization (ACO), this study discusses their applications in simulating and optimizing emergency evacuation scenarios under various constraints, interactions, and objectives. A systematic literature survey forms the backbone of this study, highlighting the diverse applications and innovations in swarm intelligence for emergency evacuation. The findings underscore the novel aspects of these algorithms, including customized objective functions, solution encodings, and effective hybridization techniques. Through case studies, the paper demonstrates the effectiveness of these techniques in critical aspects of emergency management, such as planning egress routes, locating shelters, and organizing disaster response operations. Moreover, the current limitations emphasizing the untapped potential of swarm intelligence in enhancing emergency evacuation operations are critically discussed. This survey concludes by offering a structured overview of the main findings revealed and proposing future research opportunities in applying swarm intelligence for more effective emergency evacuation planning in the following years.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 102009"},"PeriodicalIF":8.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167259","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

Combined model assisted evolutionary algorithm with complementary fill sampling criterion for expensive multi/many-objective optimization 基于互补填充采样准则的组合模型辅助进化算法用于昂贵的多目标优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-27 DOI: 10.1016/j.swevo.2025.101980

Qiuzhen Wang , Feng Xie , Yuan Liu , Juan Zou , Jinhua Zheng

引用次数: 0

A Q-learning-assisted memetic algorithm for joint vehicle scheduling problem for harvesting and transportation in smart agriculture 智能农业收获运输联合车辆调度问题的q学习辅助模因算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-27 DOI: 10.1016/j.swevo.2025.102007

Xiang Guo , Quan-Ke Pan , Wei Zhang , Zhong-Hua Miao , Xue-Lei Jing , Hong-Yan Sang

{"title":"A Q-learning-assisted memetic algorithm for joint vehicle scheduling problem for harvesting and transportation in smart agriculture","authors":"Xiang Guo , Quan-Ke Pan , Wei Zhang , Zhong-Hua Miao , Xue-Lei Jing , Hong-Yan Sang","doi":"10.1016/j.swevo.2025.102007","DOIUrl":"10.1016/j.swevo.2025.102007","url":null,"abstract":"<div><div>As smart agriculture continues to advance, the integration of agricultural activities with intelligent vehicle technologies is offering significant opportunities while posing new challenges. This paper focuses on the harvesting and transportation joint vehicle scheduling problem (HTJVSP) in smart agriculture, aiming to minimize the maximum completion time. The study proposes a joint vehicle scheduling model and introduces a novel solution approach based on a Q-learning-assisted memetic algorithm (Q-MA). The Q-MA algorithm features a hybrid initialization strategy that generates a diverse and high-quality initial population. During the evolutionary phase, three tailored crossover strategies are proposed, specifically designed to align with the unique characteristics of HTJVSP. These strategies enhance the exploration of the search space and promote faster convergence. In the local search phase, Q-learning acts as an adaptive decision- making agent, dynamically selecting the most effective operator from four specialized local search methods, thereby improving solution refinement and accelerating convergence. Finally, the experimental results and ANOVA analysis confirm that the Q-MA outperforms state-of-the-art competitors from the benchmark set, demonstrating the effectiveness of the proposed algorithmic components and its superior performance in solving the HTJVSP.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 102007"},"PeriodicalIF":8.2,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146780","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 dynamic multi-objective evolutionary algorithm based on geometric prediction and vector–scalar transformation strategy 基于几何预测和矢量-标量变换策略的动态多目标进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-05-25 DOI: 10.1016/j.swevo.2025.101987

Yan Zhao, Yongjie Ma, Yue Yang

{"title":"A dynamic multi-objective evolutionary algorithm based on geometric prediction and vector–scalar transformation strategy","authors":"Yan Zhao, Yongjie Ma, Yue Yang","doi":"10.1016/j.swevo.2025.101987","DOIUrl":"10.1016/j.swevo.2025.101987","url":null,"abstract":"<div><div>Dynamic multi-objective evolutionary algorithms (DMOEAs) have attracted significant attention from scholars due to their strong robustness and wide range of applications across various fields. A current research focus is on how to quickly track the changing Pareto Set (PS) and Pareto Front (PF); however, the distribution of optimal individuals on the PF is often overlooked. To address this issue, we propose a dynamic multi-objective evolutionary algorithm based on geometric prediction and vector–scalar transformation strategy (GPVS). By combining memory and diversity strategies, we propose a zoom-in and zoom-out prediction strategy for population range estimation based on a geometric center point. The mirror adjustment strategy is introduced as a prediction adjustment mechanism to accelerate the algorithm’s convergence. The vector–scalar transformation strategy optimizes the distribution of the evolved population following geometric prediction, ensuring that individuals carry the maximum possible evolutionary information. This strategy provides valuable population information for the next evolution. We evaluated the performance of the proposed algorithm through experimental comparisons with classical algorithms on 22 test functions, demonstrating its effectiveness and robustness in solving dynamic multi-objective optimization problems (DMOPs).</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101987"},"PeriodicalIF":8.2,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130919","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