Swarm and Evolutionary Computation最新文献

{"title":"Position deployment in a weapon system of systems: A parallel evolutionary algorithm guided by capability requirements","authors":"Ningning Wang ,&nbsp;Tingrui Liu ,&nbsp;Haibin Guo ,&nbsp;Shenmin Song","doi":"10.1016/j.swevo.2026.102300","DOIUrl":"10.1016/j.swevo.2026.102300","url":null,"abstract":"<div><div>The optimization of weapon system deployment is a crucial element in achieving strategic goals and enhancing combat effectiveness, but the varying demands for equipment capabilities during combat simulations pose challenges in constructing optimization problems and designing optimization algorithms. Reported here is the construction of a combat-effectiveness evaluation model for weapon system of systems deployment based on the evolutionary stage of the population, with the design of a multi-objective optimization problem with different characteristics, using equipment deployment positions as chromosomes. Most existing multi-objective evolutionary algorithms employ a single population evolution strategy, which fails to adequately address multi-objective optimization problems with varying problem characteristics. Instead, proposed here is a capability-demand-guided parallel evolutionary algorithm that guides the population to learn the Pareto optimal solution set by designing individual evolution strategies with different characteristics. First, based on the kill-chain generation conditions, a dual-archive set is constructed to divide the individuals in the population into two problem characteristics: one oriented toward combat rings and the other toward meeting the capabilities of combat links. Next, an elite-guided offspring-generation strategy is designed to improve the quality of candidate solutions by quantifying individual quality to guide the global search direction of the current population. Finally, the environmental selection operator is used to design a combination strategy to reorganize the two archive sets into the evolved population. The reliability and effectiveness of the proposed algorithm are verified through case studies, achieving a first-place ranking in multiple simulation comparison experiments with the shortest runtime. However, the algorithm’s control parameters need to be adjusted to adapt to changes in the complexity of combat scenarios.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102300"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147398169","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 memetic ant colony optimization algorithm for the large-scale pickup and delivery problem with time windows","authors":"Thai Khac Nguyen,&nbsp;Nam Hoai Nguyen,&nbsp;Ngoc Hoang Luong","doi":"10.1016/j.swevo.2026.102295","DOIUrl":"10.1016/j.swevo.2026.102295","url":null,"abstract":"<div><div>Hybridizations between metaheuristics and local search methods have gained increasing attention in recent years for solving the Pickup and Delivery Problem with Time Windows (PDPTW), an NP-hard problem with extensive ranges of real-world applications. The primary objective of the PDPTW is to construct scheduling solutions that minimize the number of vehicles used to successfully pick up and deliver all customer requests. When multiple solutions require the same number of vehicles, secondary criteria such as travel cost and service time are used to identify the preferable solution. In this article, we propose a hybrid framework consisting of three key components: 1) the Enhanced Constructive Ant Colony Optimization (EC-ACO) algorithm as a global search method which traverses the schedule search space; 2) the Adaptive Guided Ejection Search (AGES) which focuses on reducing the number of vehicles; and 3) the Large Neighborhood Search (LNS) which aims to optimize the overall solution cost. The experimental results demonstrate the efficacy of this approach on Li and Lim’s benchmarks, showing superior performance over other state-of-the-art methods by achieving better results in 186 out of 354 instances. Notably, the performance gap becomes more significant as the instance size increases. Source code is available at: <span><span>https://github.com/ELO-Lab/PDPTW-ECACO</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102295"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039033","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 surrogate-assisted evolutionary algorithm with multi-perspective infill sampling for expensive super-many-objective optimization problems","authors":"Xiaotong Liu ,&nbsp;Chaoli Sun ,&nbsp;Hao Wang ,&nbsp;Liming Zhang","doi":"10.1016/j.swevo.2025.102275","DOIUrl":"10.1016/j.swevo.2025.102275","url":null,"abstract":"<div><div>Surrogate-assisted many-objective evolutionary algorithms (SAMaOEAs) have become a pivotal method for solving expensive many-objective optimization problems (EMaOPs), where the cost of objective evaluations is computationally prohibitive. Training a surrogate model for each objective function is an intuitive approach, as it effectively approximates the landscape of that objective function. However, this approach tends to suffer from error accumulation as the number of objective functions increases. In this modeling approach, a straightforward way to mitigate error accumulation is to further add appropriate training samples for the surrogate model of each objective function. Due to the heterogeneous characteristics of different objective functions, identifying and adding informative training samples requires a substantial number of objective evaluations. Therefore, we propose a surrogate-assisted evolutionary algorithm with multi-perspective infill sampling (MP-SAMaOEA), in which only a small number of solutions are selected for objective evaluations to enhance the predictive ability of the surrogate model. A multi-perspective infill sampling is first presented in MP-SAMaOEA. Specifically, angle and Euclidean distance are adopted to estimate the performance of solutions in the objective space and the decision space, respectively. The non-dominated sorting is then conducted based on the above two indicators, and a subset of non-dominated solutions is selected. Additionally, a <span><math><mi>k</mi></math></span>-means-assisted diversity enhancement strategy is proposed in the surrogate-assisted optimizer to balance diversity and convergence. Experimental results on the WFG and DTLZ benchmark suites, as well as a real-world application, demonstrate that MP-SAMaOEA outperforms the comparative algorithms, particularly in solving expensive super-many-objective optimization problems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102275"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147398164","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":"Evaluation-based multi-objective optimization for responsive-resilient supply chain network under hybrid uncertainty","authors":"Yurong Guo,&nbsp;Keping Zhou","doi":"10.1016/j.swevo.2025.102274","DOIUrl":"10.1016/j.swevo.2025.102274","url":null,"abstract":"<div><div>Designing supply chains that remain responsive and resilient under hybrid uncertainty constitutes a complex multi-objective optimization challenge. This study introduces an evaluation-driven evolutionary framework that integrates metaheuristic search with performance feedback learning. A multi-objective mixed-integer programming model is formulated to simultaneously minimize total cost and maximize responsiveness and resilience. To effectively manage stochastic and fuzzy uncertainty, an improved fuzzy robust stochastic programming (IFRSP) model is developed, incorporating adaptive penalty terms to enhance feasibility robustness. A novel hybrid optimization–evaluation mechanism couples multi-objective optimization framework with a CRITIC–TOPSIS evaluator, where real-time performance feedback dynamically guides the evolutionary process toward high-quality Pareto solutions. Four representative multi-objective meta-heuristic algorithms are embedded and benchmarked. Computational results reveal that the proposed framework exhibits superior convergence diversity, stability, and decision robustness compared to classical approaches. The proposed methodology establishes a self-adaptive learning mechanism between optimization and evaluation, advancing the integration of metaheuristic evolution, performance assessment, and hybrid uncertainty modeling. This work offers a generalizable paradigm for intelligent decision-making in large-scale, uncertainty-driven optimization environments.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102274"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915142","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 large-scale multi-objective evolutionary neural network model considering robustness for short-term wind power prediction","authors":"Jianhua Zhu ,&nbsp;Yaoyao He","doi":"10.1016/j.swevo.2026.102308","DOIUrl":"10.1016/j.swevo.2026.102308","url":null,"abstract":"<div><div>Accurate prediction of wind power generation plays an essential role in the efficient operation of wind farms and power system. However, traditional deep learning-based models fail to fully utilize the temporal information and lack consideration of prediction robustness. This paper proposes a novel multi-objective evolutionary neural network model considering robustness to obtain high-quality wind power prediction results. In this approach, a dynamic temporal neural network (DTNN) that divides multiple time steps is proposed to extract critical temporal information of wind power while avoiding redundant information. Then, a multi-objective optimization (MOO) framework considering both accuracy and robustness is designed to drive the training of DTNN, which directly improves the ability of model to face highly volatile scenarios in an internally updated way. Finally, given the excessive dimensionality of the decision variables, we propose a novel large-scale multi-objective evolutionary algorithm to solve this MOO. Large-scale multi-objective multi-evolutionary state competitive swarm optimizer (LMMSCSO) adaptively implements convergence and diversity-based competitive learning strategies to improve global search and escape local optimality by quantifying evolutionary states. Experiments on real world datasets demonstrate that the proposed method effectively reduces the average forecasting error by 12.5% compared to existing benchmarks.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102308"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079227","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 two-stage multimodal multiobjective evolutionary algorithm using Voronoi diagram and modality detection","authors":"Tianzi Zheng ,&nbsp;Jianchang Liu ,&nbsp;Yaochu Jin ,&nbsp;Yuanchao Liu ,&nbsp;Wanting Yang","doi":"10.1016/j.swevo.2026.102305","DOIUrl":"10.1016/j.swevo.2026.102305","url":null,"abstract":"<div><div>In real-world applications, multimodal multiobjective optimization problems (MMOPs), where multiple Pareto optimal sets in the decision space may be mapped to the same Pareto front in the objective space, pose a grand challenge to optimization. In contrast to solving traditional multiobjective optimization problems where the focus is on the convergence and diversity of the solutions in the objective space, it also requires to pay attention to the diversity of the solutions in the decision space in solving MMOPs. To effectively tackle MMOPs, this work proposes a two-stage multimodal multiobjective evolutionary algorithm based on the Voronoi diagram and modality detection, called MMEA/VM. The proposed MMEA/VM consists of a two-stage optimization process, with the first stage focusing on increasing the diversity of the solution set in the decision space and the second stage achieving diversity and convergence in the objective space. As the first-stage search strategy, a Voronoi diagram-based diversity enhancement mechanism is designed to effectively explore the entire decision space by making use of the Voronoi neighbors. Based on a modality detection strategy that identifies the modality to which each candidate is assigned, the second stage adopts a decomposition-based search mechanism for the objective space together with a novel environmental selection method. By seamlessly interleaving the two-stage search processes, MMEA/VM strikes a good balance between exploration and exploitation in both the decision and objective spaces. Experimental studies are conducted on two benchmark suites and a real-world problem. The experimental results on five metrics demonstrate that MMEA/VM has higher competitiveness in comparison with state-of-the-art multimodal multiobjective evolutionary algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102305"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397634","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-strategy multi-population cooperative optimization algorithm for drinking water pollution source identification","authors":"Han Wang ,&nbsp;Qinghua Wu ,&nbsp;Yang Ge ,&nbsp;Zhijun Ren ,&nbsp;Xuesong Yan","doi":"10.1016/j.swevo.2026.102302","DOIUrl":"10.1016/j.swevo.2026.102302","url":null,"abstract":"<div><div>The occurrence of sudden drinking water pollution incidents can cause severe disasters and significant social losses. To ensure real-time monitoring of water quality and safeguard drinking water safety, it is essential to promptly detect pollution, locate the pollution source, and implement emergency responses. The accuracy of pollution source identification directly determines whether safety risks can be minimized. Existing optimization algorithms for solving the water pollution source identification problem often struggle to balance convergence and diversity and are trap into local optima. As a result, their performance is often unstable. To overcome these defects, this paper proposes a multi-strategy multi-population cooperative optimization algorithm (MMCOA) for drinking water pollution source identification. Based on the characteristics of the drinking water pollution source identification problem, multiple improved search strategies are designed to guide the algorithm in global search. Experimental results show that, under various pollution scenarios in pipeline networks of specific scales, the proposed strategies are effective, and the accuracy of source identification achieved by the proposed algorithm significantly surpasses that of other comparable algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102302"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147398166","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":"Transformer-accelerated multi-objective high-dimensional multi-fractional-order optimization algorithm for proportional-integral controller parameters tuning of doubly-fed induction generators","authors":"Linfei Yin,&nbsp;Bowen Zhao,&nbsp;Fang Gao","doi":"10.1016/j.swevo.2026.102325","DOIUrl":"10.1016/j.swevo.2026.102325","url":null,"abstract":"<div><div>To address the slow tuning of proportional-integral (PI) controller parameters for doubly-fed induction generators (DFIGs) using current multi-objective optimization algorithms (MOOAs), the present study proposes a Transformer-accelerated multi-objective high-dimensional multi-fractional-order optimization algorithm (Transformer-MOHDMFOO). The Transformer-MOHDMFOO incorporates Transformer-based acceleration into the iterative process by replacing part of the iterations with the predictive capabilities of the Transformer, thereby significantly reducing computational time. Simulation experiments are conducted on the Transformer-MOHDMFOO under three operating conditions: step wind speed, sinusoidal wind speed, and power grid voltage sag. Meanwhile, the Transformer-MOHDMFOO is compared with four multi-objective optimization algorithms: the multi-objective high-dimensional multi-fractional-order optimization (MOHDMFOO), the multi-objective gray wolf optimizer (MOGWO), the multi-objective particle swarm optimizer (MOPSO), and the multi-objective grasshopper optimization algorithm (MOGOA). The experimental results validate the feasibility and reliability of the PI parameters tuned by the Transformer-MOHDMFOO. The Transformer-MOHDMFOO consumes 75% less time than the MOHDMFOO and 36% less time than the MOGWO, MOPSO, and MOGOA. The Transformer-MOHDMFOO algorithm dominates the four comparison algorithms on the Pareto frontier. In terms of reactive power error, the objective function values of the solution set obtained by Transformer-MOHDMFOO are six orders of magnitude smaller than those of the comparison algorithms. The Pareto solution set obtained by the Transformer-MOHDMFOO performs best overall in terms of the hypervolume (HV) index, spread index, and R2 indicator. The PI parameters obtained by the Transformer-MOHDMFOO can maintain the efficient and stable operation of the doubly-fed induction generator-wind turbine system (DFIG-WTS). The Transformer-MOHDMFOO promotes the power generation efficiency of DFIGs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102325"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147398172","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 novel bio-inspired encoding for evolving cryptographic Boolean functions","authors":"Rocco Ascone ,&nbsp;Giulia Bernardini ,&nbsp;Luca Manzoni ,&nbsp;Gloria Pietropolli","doi":"10.1016/j.swevo.2026.102287","DOIUrl":"10.1016/j.swevo.2026.102287","url":null,"abstract":"<div><div>Discovering Boolean functions that satisfy properties such as balancedness and nonlinearity is a complex optimization problem, which is crucial to important cryptographic constructions like block and stream ciphers. The difficulty of this problem lies in the search space growing super-exponentially in the number of variables. Evolutionary approaches, including Genetic Algorithms (GAs) and Genetic Programming (GP), have been successfully applied to overcome this difficulty. The major drawback of these methods is that they evolve functions through encodings that are either exponential in the input size or hard to interpret. We address this problem as follows. (i) We propose a new encoding for Boolean functions as reaction systems, a bio-inspired computational model which can be directly translated into the compact and easily interpretable Disjunctive Normal Form (DNF). (ii) We design EvoBRS, an evolutionary optimization framework that exploits this new representation to discover Boolean functions with maximum nonlinearity (bent functions), possibly under the balancedness constraint. (iii) We back up our novel paradigm with a refined theoretical analysis of independent interest. (iv) We conduct a rigorous experimental study, demonstrating that EvoBRS consistently discovers diverse, highly nonlinear Boolean functions with and without the balancedness constraint. EvoBRS proves particularly effective on balanced functions, successfully identifying balanced maximally nonlinear instances and outperforming both GP and state-of-the-art GAs. All the discovered functions are returned in a compact and easily interpretable DNF. A preliminary version of this work appeared in Ascone et al., GECCO 2025.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102287"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980785","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 robust bi-objective optimization model and heuristic solution for truck-drone collaboration in humanitarian logistics under truck travel time uncertainty","authors":"Huizhen Zhang ,&nbsp;Chaoyue Pan ,&nbsp;Mitsuo Gen","doi":"10.1016/j.swevo.2026.102309","DOIUrl":"10.1016/j.swevo.2026.102309","url":null,"abstract":"<div><div>This paper studies the humanitarian supply transportation problem in post-disaster scenarios under uncertainty, which employs a truck-drone collaborative delivery to efficiently meet relief demands. We focus on the vehicle routing problem with drones (VRPD), in which each truck carries multiple drones that can independently deliver supplies to several affected areas during a single sortie and return to the truck at designated retrieval points for recharging. Unlike drones, which operate with stable flight times, trucks are subject to uncertain travel times due to post-disaster disruptions. The goal is to minimize both total delivery time and overall travel cost. To solve this complex problem, we propose a bi-objective metaheuristic combining Adaptive Large Neighborhood Search (ALNS) with <em>ϵ</em>-dominance within a multi-objective simulated annealing framework (AMOSA). The performance of the proposed method was evaluated by comparison with NSGA-II, MOEA/D and SPEA2, a state-of-the-art multi-objective optimization algorithm. Experiments were based on real-world data from the Kartal district of Turkey. Results show that the multi-visit mode can effectively reduce drone routes compared to the single-visit mode, especially as truck capacity increases. The importance of considering uncertainty is demonstrated by analyzing the impact of key uncertain parameters on the resulting solutions and by performing out-of-sample testing. Furthermore, we investigate how varying the number of drones and their flight range influence transportation system performance.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102309"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397638","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}