Swarm and Evolutionary Computation最新文献

{"title":"Hybrid MOEA with problem-specific operators for beam-hopping based resource allocation in multi-beam LEO satellites","authors":"Samuel Martínez Zamacola ,&nbsp;Francisco Luna Valero ,&nbsp;Ramón Martínez Rodríguez-Osorio","doi":"10.1016/j.swevo.2025.102174","DOIUrl":"10.1016/j.swevo.2025.102174","url":null,"abstract":"<div><div>The efficient allocation of satellite communication resources has become increasingly vital due to the dynamic and growing nature of traffic demand. The beam-hopping resource allocation technique addresses this challenge by enabling sequential and adaptive beam illumination, along with a dynamic distribution of power and bandwidth based on existing user demand. This work formulates the large-dimensional and highly constrained beam-hopping problem for low-power, low earth orbit satellites as a multi-objective optimization problem. It considers three key objectives: unserved capacity (UC), which measures the portion of traffic demand that remains unmet; extra served capacity (EC), which reflects excess traffic delivered beyond the requested demand, indicating possible inefficiencies; and time to serve (TTS), which represents the average waiting time for users in non-illuminated cells. Aiming at innovating in optimization, specialized initialization, crossover, mutation, and local search operators for multi-objective evolutionary algorithms (MOEAs) have been proposed. Performance is assessed through Hypervolume (HV) metrics and statistical confidence analysis. An extensive experimental analysis is presented first for a canonical NSGA-II algorithm, characterizing the impact of the new operators and hybrid components on performance. Then, beyond Pareto-based approaches such as NSGA-II, a study of both decomposition- and indicator-based MOEAs, namely MOEA/D and SMS-EMOA is assessed, demonstrating the generalizability of the presented approach. Compared to previous results in the literature, our hybrid approaches achieve up to 5% improvement in UC, up to 100% gains in EC, and up to 60% improvement in TTS for the best configuration.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102174"},"PeriodicalIF":8.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220907","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":"MOEA/D-BDN: Multimodal multi-objective evolutionary algorithm based on bi-dynamic niche strategy and adaptive weight decomposition","authors":"Chunliang Zhang ,&nbsp;Huang Li ,&nbsp;Shangbin Long ,&nbsp;Xia Yue ,&nbsp;Haibin Ouyang ,&nbsp;Houyao Zhu ,&nbsp;Steven Li","doi":"10.1016/j.swevo.2025.102171","DOIUrl":"10.1016/j.swevo.2025.102171","url":null,"abstract":"<div><div>Recently, multimodal multi-objective problems (MMOPs) have emerged as a prominent research focus in the field of multi-objective optimization. The key challenge in solving MMOPs is to identify multiple equivalent Pareto-optimal solution sets corresponding to discontinuous or complex Pareto fronts. To address this challenge, this paper proposes a novel multimodal multi-objective evolutionary algorithm (MOEA/D-BDN), which integrates a bi-dynamic niche strategy with an adaptive weight decomposition mechanism. Within the decomposition framework, the algorithm introduces an archiving mechanism to preserve historically outstanding individuals, thereby maintaining population diversity and convergence. Furthermore, a bi-dynamic niche distance (BDN) metric is employed to evaluate the overall density in both objective and decision spaces, enabling more effective updating and removal of solutions from the archive. To improve the uniformity of the Pareto front approximation, an adaptive weight adjustment strategy is used to dynamically guide the search direction. Experimental results on several benchmark MMOPs show that MOEA/D-BDN significantly outperforms state-of-the-art multimodal multi-objective evolutionary algorithms in terms of convergence, diversity, and distribution quality, demonstrating its effectiveness and competitiveness in handling complex MMOPs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102171"},"PeriodicalIF":8.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220906","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":"Feasibility-guided search and prediction for dynamic constrained multiobjective evolutionary optimization","authors":"Lisha Dong ,&nbsp;Qianhui Wang ,&nbsp;Qiongfang Liu ,&nbsp;Junkai Ji ,&nbsp;Ka-Chun Wong ,&nbsp;Qiuzhen Lin","doi":"10.1016/j.swevo.2025.102157","DOIUrl":"10.1016/j.swevo.2025.102157","url":null,"abstract":"<div><div>Dynamic constrained multiobjective optimization problems (DCMOPs) are characterized by the variations of both objectives and constraints over time, posing two main challenges: (1) balancing feasibility, convergence, and diversity in the evolutionary search and (2) generating an effective initial population for new environments. To address these problems, this paper proposes a dynamic constrained multiobjective evolutionary algorithm with feasibility-guided search and prediction (called FGSP), which integrates a feasibility-guided evolutionary search (FGES) and a feasible information guidance prediction (FIGP). Specifically, FGES adaptively adjusts evolutionary strategies by monitoring the proportion of infeasible solutions and a time-dependent tolerance threshold for infeasibility, such that it can perform exploration without constraints to navigate through large infeasible regions and conduct feasibility-driven exploitation to refine solutions near the constrained Pareto front, thereby balancing convergence, feasibility, and diversity. Concurrently, FIGP utilizes an artificial neural network trained on historically feasible solutions to predict a high-quality initial population for new environments, significantly accelerating adaptation to dynamic changes via pattern learned from past environments. After comparing the proposed FGSP with five state-of-the-art algorithms on the latest benchmark problems and one real-world problem, the experimental results validate the effectiveness of FGSP in obtaining feasible non-dominated solutions.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102157"},"PeriodicalIF":8.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220903","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 strategy cooperative algorithm based on state-awareness for large-scale multi-objective optimization","authors":"Hao Liu ,&nbsp;Jinhua Zheng ,&nbsp;Yaru Hu ,&nbsp;Xiaozhong Yu ,&nbsp;Junwei Ou ,&nbsp;Juan Zou ,&nbsp;Shengxiang Yang","doi":"10.1016/j.swevo.2025.102165","DOIUrl":"10.1016/j.swevo.2025.102165","url":null,"abstract":"<div><div>Large-scale multi-objective optimization problems (LSMOPs) usually involve hundreds to thousands of decision variables. When dealing with unconstrained 2-3-objective LSMOPs, multi-objective evolutionary algorithms (MOEAs) are likely to get trapped in local optima, making it difficult to ensure the diversity and convergence of solutions within limited computational resources. To tackle this challenge, we propose a strategy-cooperative algorithm based on state-awareness for large-scale multi-objective optimization, abbreviated as LMOEA-SC. In LMOEA-SC, we have designed a state-aware mechanism that can monitor the evolutionary state of the population in real-time. Based on the real-time information, LMOEA-SC flexibly switches and collaborates between the proposed learning strategy based on diversity protection competitive swarm optimization (DPCSO) and the escape strategy based on global exploration sampling (GES), thus effectively coping with different evolutionary states and challenges. The obtained statistical results, with a 73% improvement, clearly show that compared with six state-of-the-art MOEAs, LMOEA-SC has significant competitiveness in numerous large-scale multi-objective test instances with up to 2,000 decision variables.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102165"},"PeriodicalIF":8.5,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220905","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":"Survey on multi-task optimization: Towards cross-domain and asynchronous multi-task","authors":"Honggui Han ,&nbsp;Ben Zhao ,&nbsp;Xiaolong Wu ,&nbsp;Xin Li","doi":"10.1016/j.swevo.2025.102175","DOIUrl":"10.1016/j.swevo.2025.102175","url":null,"abstract":"<div><div>Multi-task optimization (MTO) accelerates the acquisition of optimal solutions for all tasks through effective knowledge transfer. To satisfy various practical demands, multiple tasks are often transformed into different types of optimization problems. Hence, there are numerous MTO variants in the MTO research community. To motivate deeper research on MTO and its variants, this paper mainly summarizes MTO and its variants from single-domain to cross-domain and from synchronous to asynchronous. First, the single-domain and synchronous MTO is classified into single-objective MTO, multi-objective MTO, constrained MTO, many-task optimization, and other variants based on the task types. Second, technical applications that employ MTO techniques to solve other types of optimization problems are also collated, which differ significantly from MTO variants. Finally, several promising research directions of MTO are presented theoretically and practically, including mining knowledge representations to minimize information loss, cross-domain MTO with multiple different task types, asynchronous MTO with inconsistent task arrival times, and an application of MTO to neural architecture search problems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102175"},"PeriodicalIF":8.5,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220904","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":"Offline reinforcement learning strategies guided meta-heuristics for scheduling bi-objective unmanned surface vessel problems with multiple constraints","authors":"Wuze Huang ,&nbsp;Kaizhou Gao ,&nbsp;Naiqi Wu ,&nbsp;Liang Zhao ,&nbsp;Renato Tinós","doi":"10.1016/j.swevo.2025.102159","DOIUrl":"10.1016/j.swevo.2025.102159","url":null,"abstract":"<div><div>This study proposes a reinforcement learning-guided meta-heuristics framework for bi-objective unmanned surface vessel (USV) scheduling problems under complex marine constraints, aiming to minimize the maximum completion time and total collision risk index, simultaneously. First, to specify the problems, a bi-objective mathematical model is developed considering three constraints, battery capacity, marine obstacles, and uncertain task executing time. Second, four meta-heuristics are used and improved to solve the focused problems. Based on the problem features, seven local search operators are designed to enhance the algorithms’ performances. Third, two state-reward strategies are designed and integrated into Q-learning and SARSA, respectively, to form four reinforcement learning (RL) algorithms. The four RL algorithms are off-line trained and employed to select the optimal local search operator during the iteration of meta-heuristics for improving the search efficiency. Finally, the study evaluates the performances of the proposed algorithms on 10 cases with different scales. The experimental results and statistical tests verify the efficiency of the local search operators. It is demonstrated that the four proposed RL algorithms can further improve algorithms’ performances. The particle swarm optimization (PSO) integrating Q-learning with the second state-reward strategy (PSO_QL2) exhibits the best competitiveness among all compared algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102159"},"PeriodicalIF":8.5,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159413","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":"Deep Q-network assisted variable neighborhood search algorithm for berth allocation considering berth shifting in dry bulk terminals","authors":"Wei Zhang ,&nbsp;Liang Qi ,&nbsp;Weili Zhao ,&nbsp;Lei Zhang ,&nbsp;Song Xue ,&nbsp;Wenjing Luan ,&nbsp;Yangming Zhou","doi":"10.1016/j.swevo.2025.102172","DOIUrl":"10.1016/j.swevo.2025.102172","url":null,"abstract":"<div><div>The expansion of global maritime trade, along with the surge in dry bulk vessel sizes, has intensified the shortage of deep-water berths. This work investigates a discrete berth allocation problem considering berth shifting in dry bulk terminals. It includes two shifting strategies: 1) load-reduction shifting, where large vessels first unload partial cargo at deep-water berths to lighten their draft, and then shift to shallow-water berths to complete operations; and 2) berth-releasing shifting, where small vessels shift from deep-water berths to shallow-water berths when a large vessel needs the space. A mixed-integer linear programming model is formulated to minimize the total vessel service time. A Deep Q-Network assisted Variable Neighborhood Search algorithm (DQN-VNS) is proposed to solve this problem. First, a Dynamic-priority-based Heuristic Initialization Strategy is proposed to generate high-quality initial solutions. Then, a Deep Q-Network is employed to guide the search by adaptively choosing the most promising neighborhood operator. Numerical experiments are conducted on real historical data from a dry bulk terminal. The results demonstrate that DQN-VNS can effectively improve search efficiency and solution quality, significantly reducing vessel service time in dry bulk terminals. This work can significantly enhance the operational efficiency of dry bulk terminals.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102172"},"PeriodicalIF":8.5,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159412","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":"Multiobjective multi-UAV path planning via evolutionary multitasking optimization with adaptive operator selection and knowledge fusion","authors":"Kai Meng,&nbsp;Binghong Wu,&nbsp;Bin Xin,&nbsp;Fang Deng,&nbsp;Chen Chen","doi":"10.1016/j.swevo.2025.102145","DOIUrl":"10.1016/j.swevo.2025.102145","url":null,"abstract":"<div><div>Path planning is crucial for UAV task execution, underpinning effective aerial reconnaissance and precision strikes. An ideal flight path must both minimize travel distance and reduce the risk of enemy detection or destruction. Due to the inherent trade-off between these objectives, multi-UAV path planning is conventionally formulated as a multiobjective optimization problem. However, as the number of obstacles, threats, and UAVs increases, the computational complexity escalates, hindering the generation of optimal path planning solutions via conventional multiobjective optimization approaches. To address this challenge, we model a multiobjective multi-UAV path planning (MOMUPP) problem that simultaneously optimizes flight distance and threat cost, with the latter quantified using line-of-sight theory and terrain occlusion effects. We further construct an auxiliary task that approximates the MOMUPP problem and develop an evolutionary multitasking framework to facilitate effective knowledge transfer between tasks. Building on this framework, we propose the evolutionary multitasking multiobjective path planning (EMMOP) algorithm. EMMOP incorporates a double deep Q-networks-based adaptive operator selection (DAOS) mechanism that dynamically selects the optimal search operators for each task based on the current evolutionary state, thereby generating high-quality offspring. Additionally, a knowledge transfer strategy based on directional information extraction and knowledge fusion (KTDF) enables efficient exchange of critical information between the main and auxiliary tasks. Experiments on 15 benchmark instances across five map scenarios indicate that EMMOP outperforms five state-of-the-art methods, enhancing hypervolume by 2.46% and pure diversity by 28.27%, while generating shorter, safer, and collision-free UAV paths with diverse trade-off solutions for decision-makers. The source code is available at <span><span>https://github.com/Leopard125/EMMOP</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102145"},"PeriodicalIF":8.5,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159411","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":"Metaheuristics for analog circuit design optimization: A survey","authors":"Abdelaziz Lberni ,&nbsp;Malika Alami Marktani ,&nbsp;Abdelaziz Ahaitouf ,&nbsp;Ali Ahaitouf","doi":"10.1016/j.swevo.2025.102170","DOIUrl":"10.1016/j.swevo.2025.102170","url":null,"abstract":"<div><div>As CMOS technology continues to scale down, the design complexity of very large-scale integrated circuits (VLSI) is rapidly increasing. Analog circuit design, in particular, remains time-consuming due to the critical impact of component dimensions on performance. Although the application of metaheuristics in analog circuit automation dates back to the 1980s, the growing complexity of analog design tasks and the need to reduce design cycles has sparked renewed interest in using metaheuristic approaches to address these challenges. In this paper, we provide a comprehensive and up-to-date review of existing studies on the application of metaheuristics in analog circuit design automation, including circuit synthesis, sizing, and layout synthesis, while assessing their effectiveness in meeting design objectives. The paper provides an in-depth discussion from the metaheuristics perspective and highlights key research directions for future exploration.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102170"},"PeriodicalIF":8.5,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159410","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 population-oriented hybrid search surrogate-assisted evolutionary algorithm for expensive constrained optimization multi-objective problems with small feasible regions","authors":"Cong Zhu ,&nbsp;Yongkuan Yang ,&nbsp;Xiangsong Kong ,&nbsp;Wenji Li","doi":"10.1016/j.swevo.2025.102161","DOIUrl":"10.1016/j.swevo.2025.102161","url":null,"abstract":"<div><div>Multi-objective optimization problems with expensive objectives and constraints frequently arise in real industries, such problems are called expensive constrained multi-objective optimization problems(ECMOPs). Due to the expensive cost of actual fitness calculations, constructing suitable surrogates for objectives and constraints is crucial for finding potentially feasible solutions. To enhance the search efficiency of surrogate-assisted multi-objective optimization algorithms in complex, small feasible regions with many decision variables, a population-oriented hybrid search surrogate-assisted evolutionary algorithm is proposed, called PHSEA. In PHSEA, the state of the current population is determined by relevance of the objective optimization and constraint violation reduction, as well as the ideal point change rate. Three search strategies are used: unconstrained, weakly constrained and strongly constrained surrogate-assisted search strategy, to search for feasible solutions. Furthermore, according to different search requirements, three archives with separate update criteria were used to construct the surrogate model for constraint functions. On this basis, we propose a population-oriented hybrid search framework that enhances the algorithm’s ability to search for potential solutions in small feasible regions. The proposed method was compared against two surrogate-assisted algorithms and three surrogate-free algorithms on 33 benchmark problems and 6 real-world engineering problems. Experimental results demonstrate that PHSEA exhibits strong competitiveness in solving ECMOPs characterized by small feasible regions and a large number of decision variables.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102161"},"PeriodicalIF":8.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119369","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}