Swarm and Evolutionary Computation最新文献

Forecasting continuous optimization algorithm performance improvement within each run 预测每次运行中持续优化算法的性能改进

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-05-01 Epub Date: 2026-04-28 DOI: 10.1016/j.swevo.2026.102399

Peter Korošec

{"title":"Forecasting continuous optimization algorithm performance improvement within each run","authors":"Peter Korošec","doi":"10.1016/j.swevo.2026.102399","DOIUrl":"10.1016/j.swevo.2026.102399","url":null,"abstract":"<div><div>This research introduces predictive models to forecast continuous optimization algorithm performance improvement during runtime, departing from conventional approaches focusing solely on final performance outcomes. These models leverage sequential, iteration-scaled data, which includes both the solution locations and their corresponding objective values observed throughout the algorithm’s execution. Using this information, the models aim to forecast performance improvements in future iterations, evaluated over a predetermined horizon points. These real-time forecasting capabilities could be utilized in algorithm configuration scenarios to provide additional insights, further enhancing algorithm performance in black-box continuous optimization problems. We evaluate several long short-term memory networks, selected for their effectiveness with sequential data, in two scenarios: development of personalized models using runs from a particular algorithm instance on a single problem instance and development models using runs from a particular algorithm instance on diverse set of instances of optimization problems. Employing the CEC2014 benchmark suite with 10-dimensional unconstrained problem instances and various configurations of Differential Evolution algorithm, our approach demonstrates promising results by exceeding a baseline forecasting model.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"105 ","pages":"Article 102399"},"PeriodicalIF":8.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147805242","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

A Hybrid Attraction–Repulsion Optimization Algorithm for Multi-Robot Task Scheduling in Intelligent Greenhouses 智能温室多机器人任务调度的吸引-排斥混合优化算法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-05-01 Epub Date: 2026-04-29 DOI: 10.1016/j.swevo.2026.102401

Jiawei Zhao , Hongbing Li , Binbin Zhou , Tingrui Zhang , Yanting Huang , Xiaoxing Zhang , Yongtao Li , Xiaoyu Ma , YuQiao Liang

{"title":"A Hybrid Attraction–Repulsion Optimization Algorithm for Multi-Robot Task Scheduling in Intelligent Greenhouses","authors":"Jiawei Zhao , Hongbing Li , Binbin Zhou , Tingrui Zhang , Yanting Huang , Xiaoxing Zhang , Yongtao Li , Xiaoyu Ma , YuQiao Liang","doi":"10.1016/j.swevo.2026.102401","DOIUrl":"10.1016/j.swevo.2026.102401","url":null,"abstract":"<div><div>In intelligent greenhouse environments, multi-robot cooperation over complex road networks and multi-stage operations involves strong coupling among task allocation, operation sequencing, and path selection. Solving these decisions separately may lead to an elongated critical path and inefficient resource utilization. This study addresses the Greenhouse Multi-Robot Task Scheduling (GMRTS) problem by developing a mathematical model aimed at minimizing the system makespan <span><math><msub><mi>C</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></math></span>. To solve this problem, a Hybrid Attraction-Repulsion Optimization Algorithm (HAROA) is proposed, which balances global exploration and local exploitation through a three-phase cooperative search mechanism consisting of the Vortex Diffusion Strategy (VDS), Gravitational Confluence Strategy (GCS), and Turbulent Transition Strategy (TTS). Experimental results on the CEC2017 benchmark suite and intelligent greenhouse scheduling scenarios show that HAROA achieves superior performance over comparative algorithms in terms of solution accuracy and <span><math><msub><mi>C</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></math></span>, together with faster convergence and higher solution stability. Further analyses confirm the effectiveness of the proposed strategies, the transferability of the method, the suitability of the <span><math><msub><mi>C</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></math></span>-oriented formulation, and the applicability of the framework to dynamic task scenarios.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"105 ","pages":"Article 102401"},"PeriodicalIF":8.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147805243","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

Adaptive swarm intelligence optimization for Unmanned Aerial Vehicle-assisted edge computing 无人机辅助边缘计算的自适应群智能优化

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-05-01 Epub Date: 2026-04-28 DOI: 10.1016/j.swevo.2026.102355

Fei Teng , Abdenacer Naouri , Nabil Abdelkader Nouri , Osama Abderrahman Gharbi , Attia Qammar , Sahraoui Dhelim , Tianrui Li

{"title":"Adaptive swarm intelligence optimization for Unmanned Aerial Vehicle-assisted edge computing","authors":"Fei Teng , Abdenacer Naouri , Nabil Abdelkader Nouri , Osama Abderrahman Gharbi , Attia Qammar , Sahraoui Dhelim , Tianrui Li","doi":"10.1016/j.swevo.2026.102355","DOIUrl":"10.1016/j.swevo.2026.102355","url":null,"abstract":"<div><div>The integration of Unmanned Aerial Vehicles (UAVs) into Mobile Edge Computing (MEC) systems presents a promising paradigm for delivering low-latency, on-demand computational services in dynamic and infrastructure-scarce environments. However, achieving efficient and reliable UAV deployment poses a significant optimization challenge. This challenge necessitates the simultaneous maximization of ground user coverage and robust inter-UAV connectivity, while reducing redundant overlap, which directly minimizes energy consumption and improves overall network efficiency under dynamic operational constraints. To address this complex multi-objective problem, this paper proposes an innovative swarm intelligence-driven optimization framework. The core of this framework is a Gradient-Based Optimization (GBO) algorithm specifically designed for UAV deployment. This algorithm uniquely integrates global exploration capabilities with local refinement mechanisms to navigate the intricate solution space effectively. Furthermore, we introduce a temporal graph modeling approach to capture and predict dynamic UAV–user interactions, enabling adaptive, real-time UAV repositioning in response to changing environmental conditions and user demands. Extensive simulation-based evaluations, conducted within a realistic simulated disaster-affected geographic area, validate the efficacy of our proposed framework. The GBO-driven approach achieves high operational performance: exceeding 85% user coverage, and significantly reducing coverage overlap by 43.7%, with superior convergence characteristics, reaching 95% of its total fitness improvement within just two iterations, indicating extremely fast early-stage convergence. It provides a scalable, adaptive service computing framework for time-critical, resource-constrained UAV-assisted MEC systems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"105 ","pages":"Article 102355"},"PeriodicalIF":8.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147805245","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

Adaptive fruit fly optimization-assisted logic-based benders decomposition for distributed parallel precast flowshop scheduling 基于自适应果蝇优化辅助逻辑的分布式并行预制流水车间调度弯管分解

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-05-01 Epub Date: 2026-04-28 DOI: 10.1016/j.swevo.2026.102403

Fuli Xiong , Muming Wu , Kaihao Zhou

{"title":"Adaptive fruit fly optimization-assisted logic-based benders decomposition for distributed parallel precast flowshop scheduling","authors":"Fuli Xiong , Muming Wu , Kaihao Zhou","doi":"10.1016/j.swevo.2026.102403","DOIUrl":"10.1016/j.swevo.2026.102403","url":null,"abstract":"<div><div>This paper addresses a distributed precast flowshop scheduling problem that minimizes a weighted combination of delivery time and production–transportation cost in heterogeneous factories with parallel production lines and shared resources. We formulate mixed-integer linear programming (MILP) and constraint programming (CP) models for small-scale instances. For larger instances, we develop two exact decomposition algorithms that exploit the problem’s hierarchical structure. The first algorithm, adaptive fruit fly optimization-assisted logic-based Benders decomposition with scheduling subproblem relaxations (AL_LBBD_SSR), enhances logic-based Benders decomposition with three key strategies: adaptive fruit fly optimization to generate high-quality initial solutions, strong lower bounds to tighten the search space, and subproblem relaxations to accelerate convergence. The second algorithm, branch-and-check with scheduling subproblem relaxations (BCH_SSR), employs a branch-and-check framework strengthened with problem-specific inequalities and effective relaxations. Computational experiments on 180 benchmark instances demonstrate that both algorithms significantly outperform direct MILP and CP approaches, achieving near-optimal solutions with average optimality gaps below 1%. AL_LBBD_SSR excels for large-scale instances with more than 80 orders, while BCH_SSR is more effective for moderate-sized problems with up to 50 orders. These results highlight the potential of combining nature-inspired metaheuristics with exact optimization for complex industrial scheduling problems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"105 ","pages":"Article 102403"},"PeriodicalIF":8.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147804675","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

Co-evolution with hierarchical decomposition for Vehicle Routing Problem with Drones 无人机车辆路径问题的协同进化与层次分解

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-05-01 Epub Date: 2026-04-27 DOI: 10.1016/j.swevo.2026.102402

Ruonan Zhai , Xuejun Zhang , Yi Mei , Tong Guo , Wenbo Du

{"title":"Co-evolution with hierarchical decomposition for Vehicle Routing Problem with Drones","authors":"Ruonan Zhai , Xuejun Zhang , Yi Mei , Tong Guo , Wenbo Du","doi":"10.1016/j.swevo.2026.102402","DOIUrl":"10.1016/j.swevo.2026.102402","url":null,"abstract":"<div><div>Drone delivery technology has made rapid advances and has seen growing real-world adoption in recent years. To address the inherent limitations of drones in payload and endurance, the collaborative delivery system between trucks and drones, formulated as the Vehicle Routing Problem with Drones (VRP-D), has attracted increasing research interest. In this paper, we introduce a novel Co-evolution with Hierarchical Decomposition (CH-SaBO) framework for VRP-D. Firstly, we propose a co-evolutionary scheme to decompose the original VRP-D into multiple subproblems, each with one truck, effectively reducing the dimensionality of the problem. We then design a hierarchical decomposition strategy for problem decomposition. First, a top-down procedure progressively partitions the solution into finer elements—route, operation, subroute, path, and arc. Then, a recursive bottom-up construction of inter-layer correlation matrices captures the temporal and spatial dependencies among these elements. Finally, k-medoids clustering is applied to the correlation matrices to further group the elements. For subproblem boundaries, we design a simple yet effective overlapping strategy to enhance solution quality. Each subproblem is then independently optimized using a surrogate-assisted bi-level optimizer, enabling efficient search within smaller, well-structured solution spaces. Extensive experiments on 51 benchmark instances demonstrate that CH-SaBO significantly outperforms state-of-the-art algorithms in both solution quality and computational efficiency. Further analyses confirm the scalability of CH-SaBO, as well as the effectiveness of its hierarchical decomposition and overlapping strategies.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"105 ","pages":"Article 102402"},"PeriodicalIF":8.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147805244","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

Dynamic auxiliary reference vector-based many-objective evolutionary algorithm with adaptive multi-population collaboration 基于动态辅助参考向量的多目标自适应多种群协同进化算法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-05-01 Epub Date: 2026-04-29 DOI: 10.1016/j.swevo.2026.102389

Zhanjie Wang , Yifei Yao , Du Cheng , Renyun Liu , Xiaojing Feng , Zhenwei Dong

{"title":"Dynamic auxiliary reference vector-based many-objective evolutionary algorithm with adaptive multi-population collaboration","authors":"Zhanjie Wang , Yifei Yao , Du Cheng , Renyun Liu , Xiaojing Feng , Zhenwei Dong","doi":"10.1016/j.swevo.2026.102389","DOIUrl":"10.1016/j.swevo.2026.102389","url":null,"abstract":"<div><div>Reference-vector-based many-objective evolutionary algorithms exhibit limited performance when addressing problems with irregular Pareto fronts. To overcome this limitation, this paper proposes a dynamic reference vector adjustment strategy that incorporates auxiliary vectors to enhance adaptability to complex Pareto front geometries. This strategy combines uniformly distributed reference vectors with adaptively adjusted auxiliary vectors, which effectively improves the approximation capability for irregular fronts. To further balance convergence and diversity, an adaptive multi-population evolutionary framework is designed, in which subpopulations with different search tendencies are defined and computational resources are dynamically allocated to achieve effective coordination between exploration and exploitation. In addition, a dimension-aware environmental selection mechanism is introduced, which adaptively switches selection strategies according to the number of objectives, thereby enabling more refined control over the trade-off between convergence and diversity. Based on these components, a unified algorithmic framework, termed A-RVEA-LS, is constructed. Comparative experiments on benchmark problems including DTLZ, MaF, and WFG against nine advanced algorithms demonstrate that A-RVEA-LS exhibits significantly superior overall performance and robustness in the majority of test cases.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"105 ","pages":"Article 102389"},"PeriodicalIF":8.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147805183","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

PSOT: Elite-Driven optimization via the Pareto principle for scalable engineering design solutions PSOT：基于帕累托原则的可扩展工程设计解决方案的精英驱动优化

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-04-01 Epub Date: 2026-04-11 DOI: 10.1016/j.swevo.2026.102357

Yahya Kord Tamandani, Mohammad Mehdi Keikha, Hassan Rezaei

{"title":"PSOT: Elite-Driven optimization via the Pareto principle for scalable engineering design solutions","authors":"Yahya Kord Tamandani, Mohammad Mehdi Keikha, Hassan Rezaei","doi":"10.1016/j.swevo.2026.102357","DOIUrl":"10.1016/j.swevo.2026.102357","url":null,"abstract":"<div><div>Optimizing complex real-world problems demands efficient, scalable solutions across diverse domains. This paper presents the Pareto Search Optimization Technique (PSOT), a novel population-based metaheuristic algorithm designed to address intricate optimization challenges by integrating the Pareto principle (80/20 Rule) into its core framework. PSOT innovatively partitions the population into elite (top 20%) and non-elite groups through systematic evaluation and dynamic sorting, strategically directing computational resources toward high-potential solutions in the elite cohort. This elite-centric search mechanism aligns with the Pareto principle, prioritizing exploration of the most promising regions of the solution space to enhance convergence rates while maintaining diversity. The algorithm’s uniqueness lies in its adaptive balance of intensification and diversification, leveraging the elite group to accelerate discovery of near-optimal solutions without premature stagnation. To validate PSOT’s efficacy, comprehensive experiments were conducted on standardized benchmark functions and real-world engineering design problems, including constrained, multi-modal, and high-dimensional scenarios. Results demonstrate PSOT’s superior performance against state-of-the-art optimization methods, achieving statistically significant improvements in solution quality (12–28% gains in objective values) and computational efficiency (35–50% reduction in function evaluations). Case studies across mechanical, structural, and energy systems further underscore PSOT’s practical applicability, consistently delivering robust, high-quality solutions. The algorithm’s scalability, simplicity, and alignment with empirical efficiency principles position it as a versatile tool for researchers and practitioners tackling complex optimization landscapes.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"104 ","pages":"Article 102357"},"PeriodicalIF":8.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147650302","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

Synthetic pertinence clustering and reinforced evolutionary algorithm for the hybrid E-commerce green location routing problem 混合电子商务绿色位置路由问题的综合针对性聚类和增强进化算法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-04-01 Epub Date: 2026-04-10 DOI: 10.1016/j.swevo.2026.102377

Chang Lv , Chaoyong Zhang , Yaping Ren , Leilei Meng , Jianzhao Wu

{"title":"Synthetic pertinence clustering and reinforced evolutionary algorithm for the hybrid E-commerce green location routing problem","authors":"Chang Lv , Chaoyong Zhang , Yaping Ren , Leilei Meng , Jianzhao Wu","doi":"10.1016/j.swevo.2026.102377","DOIUrl":"10.1016/j.swevo.2026.102377","url":null,"abstract":"<div><div>The rapid expansion of e-commerce and the widespread adoption of round-the-clock shipping operations have significantly intensified environmental concerns, particularly regarding carbon emissions from freight transportation. This paper addresses a newly emerging logistics challenge termed the hybrid e-commerce green location routing problem (HEGLRP), which integrates two distinct distribution modes: large-batch deliveries to offline retailers and small, stochastic shipments to online customers. HEGLRP aims to simultaneously minimize logistics costs and carbon emissions, thereby supporting the development of more sustainable e-commerce supply chains. To this end, we establish mathematical models for the HEGLRP and design a synthetic pertinence (SP) clustering strategy considering both the distance and demand pertinence. A synthetic pertinence reinforced multi-objective evolutionary algorithm based on decomposition (SPR-MOEA/D) is developed for the simultaneous optimization of logistics costs and carbon emissions. Extensive experiments are conducted on benchmark instances and real-world cases of varying scales. The results demonstrate that SPR-MOEA/D consistently outperforms state-of-the-art algorithms, achieving carbon emission reductions of up to 11.63% with only marginal increases in logistics costs, typically below 1%. The trade-off ratio frequently exceeds 1000% in large-scale instances. Comparative analysis confirms SP clustering outperforms classical methods, particularly in large instances. DRC-CMEM module analysis yields practical insights for low-carbon logistics planning. A real-world case study further validates SPR-MOEA/D’s advantages, deriving actionable management implications for balancing economic and environmental objectives in e-commerce logistics systems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"104 ","pages":"Article 102377"},"PeriodicalIF":8.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147650304","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

Instance-driven evolution of constructive heuristic ensemble for the stochastic resource allocation problem with time windows 带时间窗随机资源分配问题的建设性启发式集成实例驱动演化

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-04-01 Epub Date: 2026-04-06 DOI: 10.1016/j.swevo.2026.102381

Danjing Wang , Bin Xin , Jingyu Zhang , Qing Wang , Jia Zhang

{"title":"Instance-driven evolution of constructive heuristic ensemble for the stochastic resource allocation problem with time windows","authors":"Danjing Wang , Bin Xin , Jingyu Zhang , Qing Wang , Jia Zhang","doi":"10.1016/j.swevo.2026.102381","DOIUrl":"10.1016/j.swevo.2026.102381","url":null,"abstract":"<div><div>This paper investigates the stochastic resource allocation problem with time windows (SRA-TW), which is widely encountered in complex systems. In SRA-TW, the assignment of each resource to each task is limited within a time window, and the task completion is described by a time-dependent success probability, aiming to maximize the total expected reward of tasks. To address diverse SRA-TW scenarios, an efficient and general-purpose solving method is urgently needed. We propose an ensemble of multiple constructive heuristics (CHs), which preserves the computational efficiency of individual CHs and exploits their complementarity for superior overall performance. A three-level instance-driven evolution framework (IDEF) is further proposed, where intractable SRA-TW instances guide the adaptive evolution of the ensemble. At the bottom level, a radial-basis-function-network-based CH (RCH) is designed to construct a decision scheme for each instance rapidly, ensuring feasibility through incremental handling of temporal constraints. At the medium level, an evolutionary meta-optimization algorithm (EMOA) is proposed to simultaneously search for an ensemble of RCHs (E-RCH) capable of solving multiple instances. At the top level, intractable instances are iteratively exploited to drive the EMOA to generate new RCHs. By integrating these RCHs and refining them using historical instances, the E-RCH is progressively enhanced in generalization. Experimental results indicate that the E-RCHs built via IDEF can quickly construct decision schemes with higher expected rewards across various test instances, outperforming state-of-the-art algorithms for related problems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"104 ","pages":"Article 102381"},"PeriodicalIF":8.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147650306","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-objective evolutionary algorithms for solving a green multi-item fixed-charge 5D transportation problem under intuitionistic fuzzy uncertainty 直觉模糊不确定性下绿色多项目固定收费5D运输问题的多目标进化算法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-04-01 Epub Date: 2026-03-30 DOI: 10.1016/j.swevo.2026.102368

Ekata Jain, Jayesh M. Dhodiya

{"title":"Multi-objective evolutionary algorithms for solving a green multi-item fixed-charge 5D transportation problem under intuitionistic fuzzy uncertainty","authors":"Ekata Jain, Jayesh M. Dhodiya","doi":"10.1016/j.swevo.2026.102368","DOIUrl":"10.1016/j.swevo.2026.102368","url":null,"abstract":"<div><div>As transportation sustainability becomes an important global need, there is a growing demand for models that enable greener and more responsible logistics planning. The objective of this study is to develop a comprehensive decision-support model for sustainable multi-stage transportation systems under uncertainty. To achieve this, a three-stage green multi-objective, multi-item fixed-charge five-dimensional transportation problem is proposed that embeds sustainability directly into transportation decisions. A major advancement is the first-time integration of driver behavior into carbon emission calculations, showing that driver-specific factors significantly influence emissions. To reflect practical conditions, the model uses trapezoidal intuitionistic fuzzy numbers to represent uncertainty in transportation parameters. Due to the high dimensionality and conflicting objectives of the proposed model, classical techniques become inadequate; therefore, evolutionary methods, namely NSGA-II and NSGA-III, are employed to explore the solution space and generate diverse Pareto-optimal solutions. To support their implementation, feasible initial population generation technique and feasibility-preserving mutation operator are developed, offering novel algorithmic contributions. A real agricultural transportation case study from India validates the framework using data from Mappls Map and the official Indian Railways website. The findings indicate that NSGA-III consistently outperforms NSGA-II, yielding far superior Pareto fronts and objective trade-offs, with 98 solutions compared to only 2 produced by NSGA-II for the proposed model. The results also highlight the significant roles of vehicle, route, and driver in shaping carbon emissions, providing actionable insights for sustainable transport design. Sensitivity analysis confirms the robustness of the model, and performance metrics demonstrate the superiority of NSGA-III across all evaluation criteria.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"104 ","pages":"Article 102368"},"PeriodicalIF":8.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147602321","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