Swarm and Evolutionary Computation最新文献_第2页

Evolutionary resilience in open multiagent optimization: From preset trust to emergent defense 开放多智能体优化中的进化弹性：从预设信任到紧急防御

IF 9.6 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-10-01 Epub Date: 2026-09-27 DOI: 10.1016/j.swevo.2026.102554

Haoran Zhang, Lining Xing

{"title":"Evolutionary resilience in open multiagent optimization: From preset trust to emergent defense","authors":"Haoran Zhang, Lining Xing","doi":"10.1016/j.swevo.2026.102554","DOIUrl":"10.1016/j.swevo.2026.102554","url":null,"abstract":"<div><div>Can defensive behavior in multiagent optimization emerge through evolutionary competition rather than being pre-installed? We study this question in open, adversarial environments where agents can enter, leave, or be compromised, and where the global optimum drifts continuously as the agent pool changes, thereby creating an endogenous fitness landscape. We formulate a self-organizing three-layer dynamics: a fast optimization layer converging toward the moving target, a slow evolutionary layer in which defensive and undefended strategies replicate based on locally observable optimization performance, and a perturbation layer capturing random turnover and Byzantine attacks. Under a time-scale separation, we establish conditions under which the defensive strategy becomes evolutionarily resilient, identifying a critical attack intensity characterized in terms of the replacement rate, the exploration rate, the algebraic connectivity of the communication graph, and the condition number of the objective functions. Extensive simulations on diverse topologies confirm the spontaneous emergence of a resilient backbone without centralized provisioning, reveal a sharp phase transition in the strategy distribution, and demonstrate the hybridization of gradient-based optimization with fitness-driven replication. Our framework establishes design principles for self-organizing resilience in dynamic multiagent optimization, with implications for evolutionary dynamic optimization, swarm robotics, and federated learning.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"109 ","pages":"Article 102554"},"PeriodicalIF":9.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149157328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Taxiway path planning in airports based on Elite-guided Dynamic Lévy-flight Particle Swarm Optimization 基于精英引导的动态lcv -flight粒子群优化的机场滑行道路径规划

IF 9.6 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-10-01 Epub Date: 2026-10-12 DOI: 10.1016/j.swevo.2026.102541

Xujie Lv, Lirong Zhang, Yi Lin

{"title":"Taxiway path planning in airports based on Elite-guided Dynamic Lévy-flight Particle Swarm Optimization","authors":"Xujie Lv, Lirong Zhang, Yi Lin","doi":"10.1016/j.swevo.2026.102541","DOIUrl":"10.1016/j.swevo.2026.102541","url":null,"abstract":"<div><div>Airport taxiway path planning is crucial to airport surface operations but involves complex taxiway networks, temporal separation requirements, and potential taxiing conflicts. Standard PSO and conventional variants often suffer from premature convergence, insufficient population diversity, and limited adaptability to constrained and discrete search spaces. To address these limitations, an Elite-guided Dynamic Lévy-flight Particle Swarm Optimization algorithm (EDL-PSO) is developed to solve the airport taxiway path planning problem. The EDL-PSO integrates fitness-driven momentum adjustment, diversity-enhanced perturbation, and elite-guided redistribution. A dynamic inertia weight strategy adjusts particle momentum according to population fitness improvement to balance global exploration and local exploitation. Meanwhile, a diversity-triggered Lévy-flight mutation strategy introduces long-range perturbations to recover exploratory capability under population concentration. Furthermore, an elite-guided restart strategy reinitializes non-elite particles around high-quality solutions while preserving useful search information under persistent stagnation. Experiments are conducted on the CEC 2017 benchmark suite and real airport scenarios. The EDL-PSO achieves the best overall ranking among the state-of-the-art algorithms on the CEC 2017 benchmark suite, with superiority confirmed by statistical analysis. In the representative real airport scenario, the average taxiing time is reduced to 151.84, with a best result of 145.63 and a standard deviation of 2.85. Compared with ESD-PSO, which obtains the lowest average taxiing time among the comparison PSO variants, the EDL-PSO reduces the average taxiing time by approximately 11.7%, demonstrating the effectiveness and stability under realistic airport surface operations. The proposed conflict-aware metaheuristic method can be applied to support taxiway path planning and conflict avoidance.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"109 ","pages":"Article 102541"},"PeriodicalIF":9.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149212540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-parameter inversion of coupled stress–seepage–damage geotechnical models using a hybridized differential evolution–particle swarm optimization framework 基于混合差分演化-粒子群优化框架的应力-渗流-损伤耦合岩土模型多参数反演

IF 9.6 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-10-01 Epub Date: 2026-10-08 DOI: 10.1016/j.swevo.2026.102564

Serges Mendomo Meye, Paul Fabrice Nguema, Pieride Mabe Fogang, Serge Engueno Awono

{"title":"Multi-parameter inversion of coupled stress–seepage–damage geotechnical models using a hybridized differential evolution–particle swarm optimization framework","authors":"Serges Mendomo Meye, Paul Fabrice Nguema, Pieride Mabe Fogang, Serge Engueno Awono","doi":"10.1016/j.swevo.2026.102564","DOIUrl":"10.1016/j.swevo.2026.102564","url":null,"abstract":"<div><div>Geotechnical back-analysis of multi-physical fields faces mathematical challenges due to the high nonlinearity, non-convexity, and potential ill-posedness of objective functions mapping parameter interactions. This paper addresses the problem of simultaneously estimating geomechanical and hydraulic parameters within a non-linear stress–seepage–damage constitutive model. To mitigate the risks of premature convergence and local optima entrapment common to isolated metaheuristics, we present a hybridized optimization framework combining Differential Evolution (DE) and Particle Swarm Optimization (PSO). The framework utilizes a parallel dual-population topology governed by a stagnation-triggered information-exchange mechanism. The convergence dynamics and stability of the hybridized DE-PSO solver are evaluated over 30 independent realizations against multi-modal test functions, demonstrating competitive performance, particularly in terms of peak precision and standard deviation profiles, alongside state-of-the-art baselines including CMA-ES and SHADE. A multi-parameter back-analysis interface is compiled in C++, establishing a direct link with an implicit finite-element forward engine. The framework is evaluated using a five-dimensional (5D) inversion vector encompassing elastic modulus, Poisson’s ratio, cohesion, friction angle, and hydraulic conductivity. Sensitivity testing under a 5% zero-mean white Gaussian noise contamination indicates that the framework can successfully navigate complex error surfaces under these specific stochastic conditions. This study provides a controlled synthetic numerical evaluation for multi-variable parameter identification, offering a competitive computational approach for coupled environmental geotechnics.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"109 ","pages":"Article 102564"},"PeriodicalIF":9.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149157272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Serial distributed heterogeneous flowshop group scheduling with sequence-dependent setup times via adaptive rapid-evaluation memetic optimization 基于自适应快速评估模因优化的序列相关异构串行分布式流水车间群调度

IF 9.6 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-10-01 Epub Date: 2026-10-13 DOI: 10.1016/j.swevo.2026.102567

Zhenduo Han, Chao Lu, Biao Zhang, Guohui Zhang, Zhengfeng Li

{"title":"Serial distributed heterogeneous flowshop group scheduling with sequence-dependent setup times via adaptive rapid-evaluation memetic optimization","authors":"Zhenduo Han, Chao Lu, Biao Zhang, Guohui Zhang, Zhengfeng Li","doi":"10.1016/j.swevo.2026.102567","DOIUrl":"10.1016/j.swevo.2026.102567","url":null,"abstract":"<div><div>Serial dual-workshop systems are common in multi-stage manufacturing. Printed circuit board assembly (PCBA) is a typical example, where surface mount technology (SMT) and dual in-line package (DIP) workshops are arranged in sequence. Scheduling such systems is difficult because jobs are processed on distributed heterogeneous lines, organized by group technology (GT), and affected by sequence-dependent setup times (SDST). The two workshops also need to be coordinated carefully. This paper studies the serial distributed heterogeneous flowshop group scheduling problem with SDST (SDHFGSP-SDST). The objective is to minimize the makespan. A mixed-integer linear programming (MILP) model is first built to describe and solve small-scale instances. For larger instances, a population-state-guided adaptive rapid-evaluation memetic algorithm (ARMA) is developed. ARMA uses problem-specific constructive initialization, an elite-archive-guided restart strategy, and Q-learning-based adjustment of operator probabilities. To reduce evaluation cost, a dual-level rapid evaluation method is designed. It evaluates candidate moves at both the family and job levels under the serial dual-workshop structure. Computational experiments are conducted on benchmark instances. The results show that the MILP model can solve small-scale cases, while ARMA obtains stable and high-quality solutions for large-scale cases with much lower computational cost. The proposed method provides an effective approach for serial multi-stage scheduling with distributed heterogeneity and family-based production.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"109 ","pages":"Article 102567"},"PeriodicalIF":9.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149212541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Many objective test suite for perishable goods distribution with product-specific perishability models 许多针对易腐货物分布的客观测试套件具有特定于产品的易腐模型

IF 9.6 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-10-01 Epub Date: 2026-10-03 DOI: 10.1016/j.swevo.2026.102555

Member Joy Usigbe, Alok Singh, Yushin Ha, Rammohan Mallipeddi

{"title":"Many objective test suite for perishable goods distribution with product-specific perishability models","authors":"Member Joy Usigbe, Alok Singh, Yushin Ha, Rammohan Mallipeddi","doi":"10.1016/j.swevo.2026.102555","DOIUrl":"10.1016/j.swevo.2026.102555","url":null,"abstract":"<div><div>Perishable goods distribution (PGD) systems operate under tightly coupled routing, scheduling, refrigeration, and product-quality constraints, making them among the most challenging classes of multi-objective vehicle routing problems. Despite the growing application of Multi-Objective Evolutionary Algorithms (MOEAs) to cold-chain logistics, the absence of benchmark test suites that simultaneously capture the structural complexity of real-world perishable goods networks continues to limit rigorous algorithm design, evaluation and fair comparative analysis. Existing routing benchmarks often neglect critical characteristics such as heterogeneous perishability behavior, asymmetric spatial clustering, time-critical delivery framework, route-position-dependent quality degradation, and product incompatibility constraints, resulting in simplified optimization landscapes that insufficiently model practical cold-chain operations. This study proposes a structured multi-objective benchmark test suite with 15 PGD instances generated through a customer-type-driven framework with explicitly controlled structural properties and difficulty characteristics to address this gap. The proposed test suite integrates demand heterogeneity, temporal feasibility pressure, multi-source incompatibility relationships, specific quality loss dynamics, and diverse spatial topologies to evaluate the performance of MOEAs under realistic cold-chain conditions. The representative state-of-the-art MOEAs are evaluated across conflicting objectives and constraints. Experimental analyses and results show performance variations across instances, thereby providing a scalable and structurally diverse benchmark test suite with baseline performance. This study provides a foundation for the development, evaluation, and reproducible comparison of future optimization methods for sustainable and efficient PGD systems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"109 ","pages":"Article 102555"},"PeriodicalIF":9.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149157275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An intelligent optimization strategy of operating parameters for stable-efficient operation in the iron ore sintering process 铁矿石烧结过程运行参数的智能优化策略

IF 9.6 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-10-01 Epub Date: 2026-09-29 DOI: 10.1016/j.swevo.2026.102551

Sheng Du, Chunyang Chu, Li Jin, Zixin Huang

{"title":"An intelligent optimization strategy of operating parameters for stable-efficient operation in the iron ore sintering process","authors":"Sheng Du, Chunyang Chu, Li Jin, Zixin Huang","doi":"10.1016/j.swevo.2026.102551","DOIUrl":"10.1016/j.swevo.2026.102551","url":null,"abstract":"<div><div>To improve the stability and efficiency of the iron ore sintering process, an intelligent optimization strategy of operating parameters is proposed. The strategy integrates operating mode perception, finished sinter ratio prediction, and multi-objective optimization to address the strong nonlinearity, time delay, and multivariable coupling of the process. A feedforward neural network is developed to predict burn-through point location and temperature for operating mode perception. A hybrid model based on bidirectional long short-term memory networks and fuzzy c-means clustering is constructed to predict the finished sinter ratio under time-varying operating modes. A plant-constrained multi-objective optimization problem is then formulated to coordinate strand velocity, main flue pressure, and sintering bed height, and standard NSGA-II is adopted as its solution method. Offline evaluation using industrial historical data indicates that the integrated framework can generate candidate operating-parameter schemes that balance the predicted normal-mode proportion and the predicted finished sinter ratio. The contribution of this study lies in the domain-specific formulation and integration of operating-mode perception, time-varying production prediction, and constrained multi-objective decision making.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"109 ","pages":"Article 102551"},"PeriodicalIF":9.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149157277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Markov Blanket-guided variable analysis with learnable activation mapping for large-scale many-objective optimization 大规模多目标优化的马尔可夫毛毯导向可学习激活映射变量分析

IF 9.6 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-09-01 Epub Date: 2026-09-11 DOI: 10.1016/j.swevo.2026.102523

Oladayo S. Ajani, Rammohan Mallipeddi, Byungchul Tak

{"title":"Markov Blanket-guided variable analysis with learnable activation mapping for large-scale many-objective optimization","authors":"Oladayo S. Ajani, Rammohan Mallipeddi, Byungchul Tak","doi":"10.1016/j.swevo.2026.102523","DOIUrl":"10.1016/j.swevo.2026.102523","url":null,"abstract":"<div><div>Large-scale many-objective optimization problems (LSMaOPs) are challenging due to the high dimensionality of the decision space and complex nonlinear relationships between decision variables and objectives. Existing decision variable analysis methods often rely on auxiliary function evaluations and typically classify variables only as convergence- or diversity-related, overlooking redundant or weakly influential variables. In addition, objective-to-decision space mappings used to reduce effective dimensionality are commonly linear or based on fixed nonlinear transformations, limiting adaptability across diverse problem landscapes. To address these limitations, this paper proposes MBLEA, a novel evolutionary algorithm featuring two complementary innovations. First, a Markov Blanket-guided decision variable analysis (MBDVA) exploits conditional independence analysis to dynamically partition decision variables into convergence-related, diversity-related, and non-intrinsic groups without requiring auxiliary function evaluations. Second, a meta-learning mechanism adapts the nonlinear transformations in the objective-to-decision space mapping online by optimizing its activation-function parameters, improving adaptability across diverse problem landscapes. Experimental results on large-scale many-objective benchmark problems and a deep reinforcement learning policy optimization task demonstrate that MBLEA achieves superior optimization performance, convergence behavior, and computational efficiency compared with state-of-the-art algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"108 ","pages":"Article 102523"},"PeriodicalIF":9.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148953569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An assessment of diverse discretization strategies for particle swarm optimization in flow shop scheduling 流车间调度中不同离散化策略的粒子群优化评估

IF 9.6 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-09-01 Epub Date: 2026-09-02 DOI: 10.1016/j.swevo.2026.102519

Helio Yochihiro Fuchigami, Rian Tavares de Mello

{"title":"An assessment of diverse discretization strategies for particle swarm optimization in flow shop scheduling","authors":"Helio Yochihiro Fuchigami, Rian Tavares de Mello","doi":"10.1016/j.swevo.2026.102519","DOIUrl":"10.1016/j.swevo.2026.102519","url":null,"abstract":"<div><div>This paper examines the effectiveness and impact of different discretization methods used to transform continuous solution representations into integer values when addressing discrete optimization problems with originally continuous algorithms. Ten discretization methods from the literature are adapted and evaluated across five flow shop scheduling problems encompassing diverse objective functions, namely makespan, flowtime, idle and waiting times, total quadratic completion time, and completion time variance, thus covering just-in-time, non-regular, and trade-off performance criteria. Particle Swarm Optimization is employed as the common metaheuristic framework for implementing and comparing these discretization strategies. The computational results are assessed using analysis of variance (ANOVA), Tukey’s statistical test, and performance profiles. The findings indicate that while discretization methods such as Largest Order Value, Mapping Method, Random-Key, and Great Value Priority achieve superior performance depending on the objective function, the Backward Transformation method consistently underperforms. This study provides an updated comparative evaluation of discretization methods in flow shop scheduling contexts and offers practical insights for selecting effective discretization strategies when applying continuous metaheuristics to discrete optimization problems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"108 ","pages":"Article 102519"},"PeriodicalIF":9.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148953570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic competitive constraint handling for constrained multi-objective optimization 约束多目标优化的动态竞争约束处理

IF 9.6 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-09-01 Epub Date: 2026-08-25 DOI: 10.1016/j.swevo.2026.102506

Wei Li, Haoying Li, Ning Yang, Shuling Yang, Ying Huang

{"title":"Dynamic competitive constraint handling for constrained multi-objective optimization","authors":"Wei Li, Haoying Li, Ning Yang, Shuling Yang, Ying Huang","doi":"10.1016/j.swevo.2026.102506","DOIUrl":"10.1016/j.swevo.2026.102506","url":null,"abstract":"<div><div>When solving constrained multi-objective optimization problems (CMOPs), traditional methods often utilize a holistic constraint violation, which obscures the unique effect of individual constraints. Recently, methods based on constraint decomposition or prioritization have been proposed to consider constraints individually. However, those methods typically rely on pre-defined evaluation rules, which lack the ability to dynamically exploit the effect of an independent constraint for enhancing the exploration of the constrained Pareto front (CPF). To address this limitation, a Dynamically Competitive Constraint Handling algorithm (DCCHT) is proposed for Constrained Multi-objective Optimization. Specifically, DCCHT decomposes a CMOP with <span><math><mi>K</mi></math></span> constraints into <span><math><mrow><mi>K</mi><mo>+</mo><mn>1</mn></mrow></math></span> distinct optimization pools, where one optimization pool considers all constraints and another <span><math><mi>K</mi></math></span> optimization pools respectively involve each constraint among the overall <span><math><mi>K</mi></math></span> constraints. The information exchange among distinct optimization pools is facilitated by a proposed bidirectional knowledge transfer strategy, which shares valuable landscape information and accelerates the overall convergence to the global feasible region. The ongoing progress of each optimization pool is dynamically controlled based on the competitiveness of the constraint. Computational resources are adaptively allocated to the optimization pool by a feedback-driven resource allocation method. More computational resources will be allocated to the optimization pool in which the considered constraint is more competitive with the exploration of the true CPF. Extensive experiments on three benchmark suites and various real-world engineering problems demonstrate the significantly better performance of DCCHT against some state-of-the-art algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"108 ","pages":"Article 102506"},"PeriodicalIF":9.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148855793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Region-assigned constraint handling for dynamic constrained multi-objective optimization 动态约束多目标优化的区域分配约束处理

IF 9.6 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-09-01 Epub Date: 2026-09-03 DOI: 10.1016/j.swevo.2026.102511

Kunjie Yu, Xu Zhu, Jing Liang, Dezheng Zhang, Caitong Yue, Li Yan

{"title":"Region-assigned constraint handling for dynamic constrained multi-objective optimization","authors":"Kunjie Yu, Xu Zhu, Jing Liang, Dezheng Zhang, Caitong Yue, Li Yan","doi":"10.1016/j.swevo.2026.102511","DOIUrl":"10.1016/j.swevo.2026.102511","url":null,"abstract":"<div><div>Dynamic constrained multi-objective optimization problems have attracted increasing attention due to their wide applications in real-world scenarios. A variety of constraint handling techniques have been developed, with each performing differently based on the problem specific characteristics. However, existing dynamic constrained multi-objective evolutionary algorithms less focus on exploiting the complementary strengths of these techniques. To address limitation, this paper proposes a region-assigned constraint handling method for dynamic constrained multi-objective optimization. The algorithm introduces an adaptive threshold dynamic adjustment mechanism for flexible historical information utilization. Moreover, the objective space is partitioned into multiple sub-regions using uniform weight vectors, each solution projected onto its matching vector. Based on the comparison of projection lengths from historical optimal solutions and current individuals, more appropriate constraint handling techniques are appropriately assigned. Meanwhile, multi-population approach is applied to balance the historical and current information. The main population uses an improved <span><math><mi>ϵ</mi></math></span>-constraint method, while the auxiliary population uses the region-assigned constraint handling method to guide the optimization process. Meanwhile, knowledge transfer and collaboration between these two populations further enhance the algorithm adaptability to dynamic environments. Experiments on the bi-objective DCP and DCF benchmark suites demonstrate that the proposed algorithm achieves superior performance compared with several advanced algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"108 ","pages":"Article 102511"},"PeriodicalIF":9.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148953520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0