Swarm and Evolutionary Computation最新文献

{"title":"Archive assisted fully informed evolutionary algorithm for expensive many-objective optimization","authors":"Jie Lin ,&nbsp;Sheng Xin Zhang ,&nbsp;Shao Yong Zheng ,&nbsp;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}

{"title":"A hybrid surrogate-assisted dual-population co-evolutionary algorithm for multi-area integrated scheduling in wafer fabs","authors":"Mengyu Jin ,&nbsp;Peng Zhang ,&nbsp;Youlong Lv ,&nbsp;Ming Wang ,&nbsp;Wenbing Xiang ,&nbsp;Hongsen Li ,&nbsp;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}

{"title":"A balance-oriented iterative greedy algorithm for the distributed heterogeneous hybrid flow-shop scheduling problem with blocking constraints","authors":"Xiuli Wu,&nbsp;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}

{"title":"Swarm intelligence applications for emergency evacuation planning: state of the art, recent developments, and future research opportunities","authors":"Razieh Khayamim ,&nbsp;Ren Moses ,&nbsp;Eren E. Ozguven ,&nbsp;Marta Borowska-Stefańska ,&nbsp;Szymon Wiśniewski ,&nbsp;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}

{"title":"Combined model assisted evolutionary algorithm with complementary fill sampling criterion for expensive multi/many-objective optimization","authors":"Qiuzhen Wang ,&nbsp;Feng Xie ,&nbsp;Yuan Liu ,&nbsp;Juan Zou ,&nbsp;Jinhua Zheng","doi":"10.1016/j.swevo.2025.101980","DOIUrl":"10.1016/j.swevo.2025.101980","url":null,"abstract":"<div><div>In this paper, we design an algorithm to address the challenges of expensive multi-objective optimization problems by improving the surrogate model and sampling criterion. Firstly, we introduce a combined model which aims to enhance the impact of points that do not play a negative role, thus improving prediction accuracy. Subsequently, we develop two complementary indicators to accommodate various shapes of Pareto frontiers to better balance convergence and diversity in the sampling criterion. Experimental results on several benchmarks show that our proposed method is highly competitive in solving expensive multi-objective optimization problems compared to other state-of-the-art algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101980"},"PeriodicalIF":8.2,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139624","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}

{"title":"A Q-learning-assisted memetic algorithm for joint vehicle scheduling problem for harvesting and transportation in smart agriculture","authors":"Xiang Guo ,&nbsp;Quan-Ke Pan ,&nbsp;Wei Zhang ,&nbsp;Zhong-Hua Miao ,&nbsp;Xue-Lei Jing ,&nbsp;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}

{"title":"A dynamic multi-objective evolutionary algorithm based on geometric prediction and vector–scalar transformation strategy","authors":"Yan Zhao,&nbsp;Yongjie Ma,&nbsp;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}

{"title":"Solving machine overload for re-scheduling of dynamic flexible job shop by adaptive tripartite game theory-based genetic algorithm","authors":"Zeyu Feng,&nbsp;Zhiyuan Zou,&nbsp;Xu Liang","doi":"10.1016/j.swevo.2025.101938","DOIUrl":"10.1016/j.swevo.2025.101938","url":null,"abstract":"<div><div>In the production process of a flexible job shop, the dynamic events could disrupt the original production scheduling plan. The existing methods typically use rescheduling, but they only ensure the resumption of normal production without considering the machine load, which would lead to a machine overload vicious cycle. This paper studies the dynamic flexible job shop scheduling problem (DFJSP) considering machine load under the constraint of machine breakdown as a dynamic event, and proposes an adaptive tripartite game theory-based genetic algorithm (ATGA). Firstly, a population initialization strategy based on a pre-scheduling scheme is designed to obtain a better initial population. Then, in order to better balance multiple objectives, a machine selection strategy based on tripartite game is designed. Finally, for improving the search ability and convergence performance of the algorithm, the adaptive probability selection strategy of binary tournament is designed. The experimental results show that the algorithm surpasses other advanced algorithms in scheduling effectiveness.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101938"},"PeriodicalIF":8.2,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131020","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}

{"title":"A systematic review of metaheuristics-based and machine learning-driven intrusion detection systems in IoT","authors":"Mohammad Shamim Ahsan ,&nbsp;Salekul Islam ,&nbsp;Swakkhar Shatabda","doi":"10.1016/j.swevo.2025.101984","DOIUrl":"10.1016/j.swevo.2025.101984","url":null,"abstract":"<div><div>The widespread adoption of the Internet of Things (IoT) has raised a new challenge for developers since it is prone to known and unknown cyberattacks due to its heterogeneity, flexibility, and close connectivity. To defend against such security breaches, researchers have focused on building sophisticated intrusion detection systems (IDSs) using machine learning (ML) techniques. Although these algorithms notably improve detection performance, they require excessive computing power and resources, which are crucial issues in IoT networks considering the recent trends of decentralized data processing and computing systems. Consequently, many optimization techniques have been incorporated with these ML models. Specifically, a special category of optimizer adopted from the behavior of living creatures and different aspects of natural phenomena, known as metaheuristic algorithms, has been a central focus in recent years and brought about remarkable results. Considering this vital significance, we present a comprehensive and systematic review of various applications of metaheuristics algorithms in developing a machine learning-based IDS, especially for IoT. A significant contribution of this study is the discovery of hidden correlations between these optimization techniques and machine learning models integrated with state-of-the-art IoT-IDSs. In addition, the effectiveness of these metaheuristic algorithms in different applications, such as feature selection, parameter or hyperparameter tuning, and hybrid usages are separately analyzed. Moreover, a taxonomy of existing IoT-IDSs is proposed. Furthermore, we investigate several critical issues related to such integration. Our extensive exploration ends with a discussion of promising optimization algorithms and technologies that can enhance the efficiency of IoT-IDSs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101984"},"PeriodicalIF":8.2,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130918","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}

{"title":"Remanufacturing scheduling under uncertainty considering remanufacturability assessment with adaptive hybrid optimization algorithm","authors":"Qinyu Jin ,&nbsp;Jifeng Xu ,&nbsp;Xiaoling Huang ,&nbsp;Xiangqi Liu ,&nbsp;Liang Huang ,&nbsp;Kang Jiang","doi":"10.1016/j.swevo.2025.101990","DOIUrl":"10.1016/j.swevo.2025.101990","url":null,"abstract":"<div><div>Remanufacturing enables the values contained in end-of-life products to be developed and utilized to the maximum extent, which is greatly significant to economic and social development. The remanufacturing process is characterized by uncertainties such as the quality of end-of-life products and the required remanufacturing time. Some studies have focused on remanufacturing scheduling under uncertainty. However, these studies ignored the direct effects of uncertainties on the assessment of remanufacturability and the selection of remanufacturing lines. Therefore, this study proposed a new decision tree-based remanufacturing scheduling model under uncertainty considering remanufacturability assessment, which constructs decision trees and combines fuzzy numbers to assess remanufacturability and select appropriate remanufacturing lines. Experiments have shown that the proposed model increases the total profits by approximately 2.8 %. To solve this model effectively, an adaptive hybrid optimization algorithm is proposed, with a new solution representation scheme, an adaptive adjustment function and a new population updating strategy. Simulated comparison experiments with other baseline algorithms and a real case study demonstrate that, the proposed algorithm has better performance in solution exploration and has superior stability in solving the remanufacturing scheduling model proposed in this study. Specifically, for improving the efficiency of remanufacturing, the proposed algorithm performs 0.5 % better than the differential evolutionary algorithm, 3.3 % better than the teaching-learning-based optimization algorithm, 0.2 % better than the extended particle swarm optimization algorithm, 1.7 % better than the improved ant colony optimization algorithm, and 2.7 % better than the simulated annealing algorithm, approximately. Finally, a real case study demonstrates the superior performance of the proposed model and algorithm in real industrial applications.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101990"},"PeriodicalIF":8.2,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124513","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}