Swarm and Evolutionary Computation最新文献

{"title":"An adaptive VNS-based evolutionary optimization for hybrid flowshop scheduling with consistent sublots","authors":"Li Yuan ,&nbsp;Hong-Yan Sang ,&nbsp;Lei-Lei Meng ,&nbsp;Biao Zhang","doi":"10.1016/j.swevo.2026.102299","DOIUrl":"10.1016/j.swevo.2026.102299","url":null,"abstract":"<div><div>Lot streaming technology plays a critical role in shortening production cycles, reducing waiting times, and enhancing production capacity. This paper addresses the hybrid flowshop scheduling problem with consistent sublots (HFSP_CS) and proposes an adaptive VNS-based evolutionary algorithm (AVNSEA) to minimize the total flow time. HFSP_CS involves multiple interdependent subproblems that need to be solved simultaneously, including lot splitting, lot sequencing, and machine allocation. To address this, a mixed integer linear programming (MILP) model is formulated. The proposed AVNSEA algorithm employs an adaptive perturbation strategy to diversify the search and explore potentially promising regions in the solution space, while embedding a variable neighborhood search (VNS)-based local search to intensify the refinement of high-quality solutions. Furthermore, a dynamic acceptance criterion is introduced to balance exploration and exploitation during the evolutionary process. Extensive tests confirm that the proposed AVNSEA algorithm offers significant advantages in solving the HFSP_CS.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102299"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079106","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":"River-land multi-modal bulk cargo transportation problem with containerization","authors":"Wei Wu ,&nbsp;Lijun Fan ,&nbsp;Ruiyou Zhang ,&nbsp;Qianli Ma ,&nbsp;Peng Jia","doi":"10.1016/j.swevo.2025.102261","DOIUrl":"10.1016/j.swevo.2025.102261","url":null,"abstract":"<div><div>This research addresses a river-land multi-modal bulk cargo transportation problem with containerization. It involves three transportation modes: inland waterway, railway, and road transportation. While heterogeneous vessels are commonly employed in inland waterway transportation, few studies have focused on the allocation of these vessels within the context of river-based multimodal transportation. Consequently, introducing decisions on container usage for bulk shipments, identifying containerization locations, and assigning heterogeneous ships to riverine channel in multimodal transportation presents significant challenges. An integer nonlinear programming model based on a directed graph, which incorporates constraints such as water depth, the availability of road and railway vehicles, and the capacity of containerization equipment throughout the planning horizon, is formulated and subsequently linearized. The objective is to minimize the total cost, including transportation, containerization, and cargo damage costs. A multiple ant colony algorithm embedded by a mathematical model is developed to solve the problem. Experiments conducted on numerous near-practical instances demonstrate the effectiveness of the solution methods. The results indicate that for medium- and large-scale instances, the methodology can achieve optimal or high-quality feasible solutions within a reasonable computation time.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102261"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980706","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":"Adaptive neighborhood reduction-based memetic algorithm for the set-union knapsack problem","authors":"Zequn Wei ,&nbsp;Jianing Yu ,&nbsp;Jin-Kao Hao ,&nbsp;Jintong Ren","doi":"10.1016/j.swevo.2026.102289","DOIUrl":"10.1016/j.swevo.2026.102289","url":null,"abstract":"<div><div>The set-union knapsack problem (SUKP) is an important NP-hard variant of the knapsack problem, where each item has a profit and is composed of multiple weighted elements. The SUKP aims to select a subset of items that maximizes the total profit while ensuring the union of their associated elements satisfies the capacity constraint. The SUKP is a challenging combinatorial optimization problem that has been widely studied for its theoretical value and practical relevance. In this work, we present a population-based memetic framework specifically designed for solving the SUKP. The proposed approach integrates an adaptive neighborhood reduction based memetic search, which strengthens intensification by embedding a dynamic profit-to-weight ratio scoring method into a solution-based tabu search. To ensure diversification, a diversity-driven greedy crossover operator and an adaptive population updating rule are developed. The algorithm requires no manual parameter tuning and remains effective across instances of widely varying sizes. Computational results on 132 commonly used benchmark instances demonstrate that our method is both competitive and robust compared with state-of-the-art algorithms. The algorithm is further applied to the SUKP-related budgeted maximum coverage problem, confirming its efficiency and generality. We also provide additional analysis on the influences of several key components of the algorithm.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102289"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039029","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":"Similarity-driven knowledge transfer algorithm for many-task capacitated vehicle routing problem","authors":"Yanlin Wu ,&nbsp;Xinyu Zhou ,&nbsp;Hui Wang ,&nbsp;Jia Zhao","doi":"10.1016/j.swevo.2026.102297","DOIUrl":"10.1016/j.swevo.2026.102297","url":null,"abstract":"<div><div>This study addresses the computational inefficiency of traditional evolutionary multitasking algorithms in solving many-task capacitated vehicle routing problems (CVRPs) by proposing a knowledge transfer optimization framework driven by dynamic similarity evaluation. Existing approaches predominantly rely on explicit knowledge transfer mechanisms based on transfer matrices, whose computational complexity escalates exponentially with increasing task quantities. To overcome this limitation, a three-phase optimization framework is developed: (1) Common features across multiple tasks are extracted through feature space mapping techniques, establishing a quantifiable similarity evaluation model; (2) An adaptive knowledge transfer feedback system is implemented, integrating a transfer-effect monitoring mechanism and dynamic weight adjustment strategy to ensure real-time optimization of knowledge source quality; (3) A hybrid crossover operation architecture is designed, combining elite solution transfer with local route optimization to reduce computational overhead. Comparative experiments conducted on a comprehensive simulation dataset (containing 99 many-task CVRP instances) and real-world logistics scenarios demonstrate the algorithm’s superior performance across multiple metrics.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102297"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039030","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 decomposition-based constrained multi-objective optimization algorithm with dynamic resource reallocation guided by niche classification","authors":"Rui Yang ,&nbsp;Minggang Dong ,&nbsp;Wenzhang Liu","doi":"10.1016/j.swevo.2026.102321","DOIUrl":"10.1016/j.swevo.2026.102321","url":null,"abstract":"<div><div>Decomposition-based constrained multi-objective evolutionary algorithms (CMOEAs) simplify complex optimization problems by decomposing them into multiple subproblems. These subproblems contribute unevenly to population optimization and demand varying computational resources across generations. However, most existing decomposition-based CMOEAs lack the prior knowledge for predetermining subproblem distributions. This leads to a suboptimal allocation of optimization weights and inflexible resource distribution, ultimately limiting their performance. To address this, we propose a niche classification strategy that identifies the distribution characteristics of local subproblems and categorizes them into distinct niches based on feasibility and dominance. This classification, updated each generation, provides dynamic prior knowledge, enabling adaptive allocation of optimization weights and computational resources tailored to each niche category. To operationalize this, we design a dual-population co-evolution framework based on decomposition, which dynamically redistributes resources among niches. Furthermore, we introduce an novel intergenerational fitness function to better assess the optimization potential of niches within the same category. By analyzing subpopulation changes across consecutive iterations, this function evaluates niche-level performance, thereby decoupling individual performance from fitness evaluation. Comprehensive experiments on 59 benchmark functions and a collaborative path planning task for multi-unmanned surface vehicles demonstrate that the proposed algorithm achieves competitive performance compared with seven state-of-the-art CMOEAs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102321"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397641","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":"Component-level knowledge transfer based on diffusion model in evolutionary multitasking","authors":"Ruilin Wang,&nbsp;Xiang Feng,&nbsp;Huiqun Yu","doi":"10.1016/j.swevo.2026.102301","DOIUrl":"10.1016/j.swevo.2026.102301","url":null,"abstract":"<div><div>Evolutionary Multitasking has proven effective in addressing multi-task optimization, with knowledge transfer playing a key role in improving algorithm performance. However, existing studies mainly emphasize the timing and methods of transfer, often constrained by specific task assumptions, while overlooking the potential of components during the process. Additionally, reliance on traditional stochastic evolutionary operators limits search efficiency. To address these limitations, this paper proposes a Diffusion-based Multifactorial Evolutionary Algorithm (D-MFEA), featuring a novel component-level knowledge transfer framework for unconstrained single-objective multi-task problems. This framework integrates a diffusion model as the transfer component, enabling efficient knowledge sharing and collaboration between evolutionary and transfer components. It demonstrates strong generalization, seamlessly adapting to and enhancing various MFEA algorithms. By generating high-quality individuals, the diffusion model facilitates positive transfer, reducing reliance on stochastic evolutionary operators and assumptions about task relationships, thereby significantly improving the efficiency of knowledge transfer. Theoretical analyses ensure the diffusion model’s ability to generate high-quality individuals, while experiments on multiple single-objective multi-task benchmarks and a real-world application demonstrate that D-MFEA achieves faster convergence. Ablation studies confirm the effectiveness and robustness of the framework’s components and analyze the impact of varying noise configurations. Extensive results show that our algorithm outperforms state-of-the-art methods.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102301"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147398168","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 reinforcement learning and heuristic-based dynamic switch migration for Low Earth Orbit satellite networks","authors":"Yong Deng ,&nbsp;Feng Yao ,&nbsp;Jianghan Zhu","doi":"10.1016/j.swevo.2026.102307","DOIUrl":"10.1016/j.swevo.2026.102307","url":null,"abstract":"<div><div>The centralized control architecture and programmable features of Software Defined Networking (SDN) present significant opportunities for optimizing Low Earth Orbit (LEO) satellite network performance. Nevertheless, the time-varying topology and non-uniform user distribution characteristics of LEO satellite networks lead to controller load imbalance, which necessitates adaptive controller-switch mapping mechanisms to maintain optimal load distribution between controllers. Most existing migration strategies overlook the overall network performance, resulting in sub-optimal migration quality. Moreover, they fail to address the issue of isolated nodes during migration, which adversely affects network reliability and security. To address these issues, a mathematical optimization model is formulated with the objectives of minimizing latency and achieving controller load balancing, subject to constraints such as controller capacity and intra-domain switch connectivity. To solve this model, we propose a dynamic switch migration algorithm based on deep reinforcement learning and heuristic method (DSM-DH), which comprises two phases: control relationship optimization and connectivity restoration. In the first stage, the deep reinforcement learning (DRL) framework with a multi-neural network architecture is employed, incorporating a dynamic <span><math><mi>ϵ</mi></math></span>-greedy strategy and a prioritized experience replay mechanism to comprehensively optimize control relationships while satisfying controller capacity constraints. In the second stage, the heuristic approach is used to address the isolated nodes that arise during the migration process. Without violating the controller capacity constraints, isolated switches are prioritized for migration to the controller with the lowest load, so as to minimize the disturbance to the control relationships optimized in the first stage, thereby achieving full connectivity among switches within each domain. Finally, simulation experiments are conducted to compare the DSM-DH algorithm with existing benchmark algorithms across several key performance metrics, including latency and load balancing. The results demonstrate that the DSM-DH algorithm can effectively improve network performance.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102307"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079107","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 multi-objective evolutionary algorithm with clustering-based archiving and adaptive search mechanism","authors":"Yiting Zeng,&nbsp;Peng Shao,&nbsp;Shaoping Zhang","doi":"10.1016/j.swevo.2025.102277","DOIUrl":"10.1016/j.swevo.2025.102277","url":null,"abstract":"<div><div>To address the challenges of unstable archiving mechanisms and the difficulty of balancing diversity and convergence in multi-objective evolutionary algorithms, this paper proposes a multi-objective evolutionary algorithm with clustering-based archiving and an adaptive search mechanism based on Harris Hawks optimization (MOCAS/HHO). Building on the framework of the Harris Hawks Optimization (HHO), MOCAS/HHO employs <em>k</em>-medoids clustering to update the archive, where representative solutions at the cluster centers are preserved to improve solution diversity. Subsequently, MOCAS/HHO identifies ‘valuable solutions’ from the archive to guide the population toward the correct search direction. Based on the proportion and saturation of the ‘valuable solutions’, a regulatory factor is introduced to perturb the escape energy <em>E</em>, enabling the algorithm to adaptively adjust its search direction. Moreover, leaders are randomly selected from the valuable solutions to enhance stability and the global search capability of MOCAS/HHO. For the performance evaluation, MOCAS/HHO is compared with 9 algorithms on 25 benchmark functions, using IGD and HV metrics and statistical analysis. MOCAS/HHO outperforms MOHHO on approximately 88 % of the selected 2–3 objective functions, while achieving superior performance on all chosen 4-objective high-dimensional functions. For the Car side impact design problem, MOCAS/HHO improves IGD by 24.3 % over MOEDO; for the Liquid-rocket single element injector design, it improves IGD by 65.95 % over MOGWO; and for Conceptual marine design, it ranks second in IGD to MOEA/D. Overall, these results indicate that MOCAS/HHO achieves a good balance between convergence and diversity across both benchmark test functions and practical engineering applications.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102277"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915143","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":"Low-cost safe path planning and exit scheduling of multi-UAV aerial refueling based on swarm intelligence","authors":"Bin Hang ,&nbsp;Pengjun Guo","doi":"10.1016/j.swevo.2026.102293","DOIUrl":"10.1016/j.swevo.2026.102293","url":null,"abstract":"<div><div>Aerial refueling technology is a crucial means of extending unmanned aerial vehicles (UAVs) mission duration and expanding operational range, garnering extensive attention. However, planning safe and cost-effective refueling routes for multiple UAVs in complex three-dimensional airspace, and achieving efficient and orderly egress after mission completion, still face technical challenges such as inadequate path safety and low egress scheduling efficiency. To address these challenges, this paper proposes a multi-agent hierarchical collaborative optimization framework that simulates group competition and cooperation to achieve task allocation and path coordination. By integrating factors such as path length, threat sources, air turbulence, altitude-dependent energy consumption, and turning loss, a multi-dimensional cost function is constructed, forming a comprehensive trajectory optimization model for UAV aerial refueling missions. Based on flight landing scheduling (FLS) theory, a dynamic time window allocation and conflict resolution mechanism is introduced, establishing a two-stage optimization architecture of ”path planning-safe egress.” Simulation results indicate that, compared to several mainstream meta-heuristic algorithms, the proposed method achieves superior path quality and higher scheduling efficiency under complex conditions, reliably accomplishing low-cost, coordinated multi-UAV refueling and safe egress operations.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102293"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039023","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":"Synergistic Particle Swarm Optimized Bio-inspired Artificial Neural Network for Fractional Analysis of tumor-immune competitive system with multiple time delays","authors":"Muhammad Wajahat Anjum ,&nbsp;Noreen Sher Akbar ,&nbsp;Muhammad Bilal Habib ,&nbsp;Taseer Muhammad","doi":"10.1016/j.swevo.2026.102284","DOIUrl":"10.1016/j.swevo.2026.102284","url":null,"abstract":"<div><div>This study introduces a novel application of machine learning based intelligent computing for developing bio-inspired neural networks and gradient backpropagation neural networks for tumor-immune interaction model. These models aim to solve nonlinear fractional cancer mathematical system described by four differential equations representing the population dynamics of cancer cells, macrophages, CD8+ T cells, and dendritic cells. Synthetic datasets were generated using the Adams-Bashforth predictor-corrector numerical method, with variations in the time delay and fractional order across each compartment. Both neural network models were trained on these datasets, divided into training and testing sets with an 80:20 ratios. Their performance was evaluated using metrics such as the R-squared score, mean squared error, and visual analyses including absolute error plots, error histograms, and loss curves. A total of six different optimizers were taken with three based on gradient based learning and three based on bio-inspired learning. The models were evaluated based on minimizing the mean squared error. The Bayesian Regularized Gradient based Neural Networks and Particle Swarm Optimized Bio-inspired Artificial Neural Network were found out to be the best performing models in the group of gradient-based and bio-inspired models respectively. However, the Particle Swarm Optimized Bio-inspired Artificial Neural Network demonstrated the highest efficiency, outperforming other gradient and bio-inspired algorithms according to statistical and graphical assessments.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102284"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039027","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}