Swarm and Evolutionary Computation最新文献_第3页

DSPDE: A synergistic integration of dynamic stratification and probabilistic escape for enhanced differential evolution optimization DSPDE：动态分层和概率逃逸的协同集成，用于增强差分进化优化

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-04-01 Epub Date: 2026-04-08 DOI: 10.1016/j.swevo.2026.102380

Chiwen Qu , Juchuan Yuan , Lupeng Zhang , Xinyue Zhang , Zhengxin Huang

{"title":"DSPDE: A synergistic integration of dynamic stratification and probabilistic escape for enhanced differential evolution optimization","authors":"Chiwen Qu , Juchuan Yuan , Lupeng Zhang , Xinyue Zhang , Zhengxin Huang","doi":"10.1016/j.swevo.2026.102380","DOIUrl":"10.1016/j.swevo.2026.102380","url":null,"abstract":"<div><div>The lack of explicit metrics for online population state assessment makes it challenging to dynamically balance exploration and exploitation in Differential Evolution (DE). To address this, this paper proposes DSPDE, a novel DE variant that introduces a closed-loop framework to achieve the synergistic integration of dynamic stratification with a probabilistic escape strategy. This framework enables algorithm self-regulation by continuously monitoring population distribution and individual stagnation, then triggering targeted structural and behavioral adjustments. Specifically, DSPDE first employs a dynamic stratification mechanism that partitions the population into elite, ordinary, and inferior subgroups based on fitness distribution. The subgroup sizes are adaptively adjusted during the evolutionary process, ensuring resource allocation aligns with evolutionary stages. Concurrently, a probabilistic escape strategy monitors individuals using a Gaussian mixture model to quantify stagnation risk. Upon detecting stagnation, it intelligently selects between an anti-gradient escape operator (for directional exploration) and a mirror resampling operator (for diversity recovery), thereby enhancing local optimum avoidance. The synergy lies in the state-aware coordination loop: stratification results inform escape probability, while escape outcomes refine subsequent stratification. This creates an adaptive “perceive-adjust-respond” cycle for population management. Extensive experiments on the CEC2022 benchmark and practical mechanical engineering problems demonstrate that DSPDE significantly outperforms state-of-the-art DE variants in convergence accuracy, speed, and robustness, with statistical verification. Theoretical analysis of the escape strategy’s exploration capability and ablation studies validating each module’s contribution are also provided.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"104 ","pages":"Article 102380"},"PeriodicalIF":8.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147650309","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 global feedback learning-based memetic algorithm for energy-aware scheduling in collaborative heterogeneous flexible job shops 基于全局反馈学习的异构柔性作业车间能量感知调度模因算法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-04-01 Epub Date: 2026-04-08 DOI: 10.1016/j.swevo.2026.102392

Hongquan Qu, Shiliang Shao, Yunhong Xu, Maolin Cai, Yan Shi, Xiaomeng Tong

{"title":"A global feedback learning-based memetic algorithm for energy-aware scheduling in collaborative heterogeneous flexible job shops","authors":"Hongquan Qu, Shiliang Shao, Yunhong Xu, Maolin Cai, Yan Shi, Xiaomeng Tong","doi":"10.1016/j.swevo.2026.102392","DOIUrl":"10.1016/j.swevo.2026.102392","url":null,"abstract":"<div><div>Industry 5.0 emphasizes the collaboration between human and robot resources in perception, decision-making, and execution, forming a dynamic and complementary cooperative mechanism. However, multi-flexible resource collaboration significantly enlarges the solution space, which increases the difficulty of resource allocation and optimization. At the same time, the matching of jobs with machines and the switching of operational states, such as startup and shutdown, directly affect energy consumption, making energy savings and cost reduction rigid requirements. To address these issues, a collaborative heterogeneous flexible job shop scheduling (CHFJS) model is formulated, with the primary objectives of minimizing makespan and energy consumption. Subsequently, a memetic algorithm based on global feedback learning (GFLMA) is proposed to solve the CHFJS problem. A total of 12 neighborhood structures are designed, and a Bayesian inference and weighting-based local search strategy is established. Additionally, energy-saving operators are specifically designed to address the problem characteristics. Finally, extensive experiments on instances of various scales are conducted to validate the effectiveness of GFLMA. The results demonstrate that the proposed algorithm outperforms state-of-the-art algorithms in more than 70% of the instances, confirming the superiority of GFLMA.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"104 ","pages":"Article 102392"},"PeriodicalIF":8.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147650389","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

Knowledge-driven large-scale multi-objective evolutionary learning for interval prediction of key quality indicators in blast furnace ironmaking process 基于知识驱动的高炉炼铁过程关键质量指标区间预测的大规模多目标进化学习

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.2026.102379

Jingchuan Zhang , Yaxue Liu , Xianpeng Wang

{"title":"Knowledge-driven large-scale multi-objective evolutionary learning for interval prediction of key quality indicators in blast furnace ironmaking process","authors":"Jingchuan Zhang , Yaxue Liu , Xianpeng Wang","doi":"10.1016/j.swevo.2026.102379","DOIUrl":"10.1016/j.swevo.2026.102379","url":null,"abstract":"<div><div>Accurate prediction interval (PI) modeling of key quality indicators in the blast furnace (BF) ironmaking process is crucial for maintaining its stable operations. Nevertheless, existing approaches often face challenges in accurately capturing highly nonlinear input–output relationships and sample-dependent heteroscedastic uncertainty commonly observed in industrial data, while suffering from severely degraded efficiency when the corresponding PI modeling task involves a large-scale search space. To address these challenges, this paper formulates the PI modeling task as a large-scale multi-objective optimization problem (MOP) that simultaneously maximizes PI coverage probability and minimizes interval width; and then proposes a dual knowledge learning-based large-scale multi-objective evolutionary algorithm (DKL-LSMOEA) to enable fast and robust PI modeling. In DKL-LSMOEA, high-quality datasets are first constructed by collecting and labeling the evolutionary process data, from which two complementary types of knowledge are learned, i.e., structural importance knowledge and directional distribution knowledge. Based on this knowledge, a knowledge-driven reproduction operator is then developed to enhance search efficiency and convergence performance within a more compact search space. Extensive experimental results demonstrate that DKL-LSMOEA outperforms state-of-the-art algorithms on both benchmark large-scale MOPs and the real-world PI modeling task in BF ironmaking, achieving a more favorable trade-off between interval coverage and interval width and thus showing strong potential for practical industrial applications.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"104 ","pages":"Article 102379"},"PeriodicalIF":8.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147650307","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

Solving electric vehicle routing problem with heterogeneous drones and no-fly zones using enhanced adaptive large neighborhood search algorithm 基于增强的自适应大邻域搜索算法求解异构无人机和禁飞区下电动汽车路径问题

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.2026.102382

Wanting Chen, Shuai Zhang, Kang Jiang

{"title":"Solving electric vehicle routing problem with heterogeneous drones and no-fly zones using enhanced adaptive large neighborhood search algorithm","authors":"Wanting Chen, Shuai Zhang, Kang Jiang","doi":"10.1016/j.swevo.2026.102382","DOIUrl":"10.1016/j.swevo.2026.102382","url":null,"abstract":"<div><div>The collaboration of electric vehicles and drones in last-mile deliveries meets the demands of a booming e-economy while promoting environmental sustainability. However, challenges arise for drones, such as fixed payload compartments unsuitable for diverse package sizes and restricted flight areas due to regulatory policies. Thus, this study proposes a novel model for the electric vehicle routing problem with heterogeneous drones and no-fly zones to tackle the mentioned challenges. First, the proposed model extends the drones to heterogeneous types with different flight endurance, payload capacity, and flight speed for real-world scenarios. Second, it defines the restricted flight area as a no-fly zone, which is close to reality. A detour strategy is devised to address the drone routes that conflict with the no-fly zones. To solve this model, an enhanced adaptive large neighborhood search algorithm with three-layer coding scheme is presented. The algorithm utilizes a longest detour removal operator to avoid local optimal solutions and integrates a no-fly zone tabu strategy to enhance global search capability. Finally, the experimental results demonstrate the effectiveness of the proposed algorithm.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"104 ","pages":"Article 102382"},"PeriodicalIF":8.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147650390","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

Differential evolution with a variance contribution ratio-based diversity enhancement mechanism for numerical optimization 基于方差贡献比的差分进化多样性增强机制的数值优化

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.2026.102359

Liqi Zhao , Liangliang Sun , Zhenghao Song , Qichun Zhang , Natalja Matsveichuk , Yuri Sotskov

{"title":"Differential evolution with a variance contribution ratio-based diversity enhancement mechanism for numerical optimization","authors":"Liqi Zhao , Liangliang Sun , Zhenghao Song , Qichun Zhang , Natalja Matsveichuk , Yuri Sotskov","doi":"10.1016/j.swevo.2026.102359","DOIUrl":"10.1016/j.swevo.2026.102359","url":null,"abstract":"<div><div>Owing to its fast convergence speed and high optimization accuracy, differential evolution (DE) has become one of the benchmark algorithms for solving continuous optimization problems. However, when tackling complex optimization problems such as high-dimensional and multimodal tasks, DE often suffers from evolutionary stagnation, insufficient global search capability, and premature convergence. To address these challenges, this paper proposes Differential Evolution with a Variance Contribution Ratio-Based Diversity Enhancement Mechanism (DE-VCR). The proposed algorithm incorporates the following three major improvements. First, an infinity-norm-based parameter generation and update mechanism is designed, which exploits information from the current best individual to guide adaptive control parameter adjustment. Second, a gamma-distribution-based mutation strategy is proposed to enhance global exploration by transforming low-fitness individuals. Finally, a population diversity enhancement mechanism based on the variance contribution ratio is introduced and combined with principal component analysis to effectively alleviate evolutionary stagnation. To evaluate the performance of DE-VCR, extensive experiments are conducted on 376 benchmark problems from the CEC2013, CEC2014, CEC2017, and CEC2022 test suites, with comparisons against various state-of-the-art DE variants and other evolutionary algorithms. The experimental results demonstrate that DE-VCR exhibits strong competitiveness in terms of optimization accuracy. In addition, the algorithm shows good practicality and robustness in real-world applications such as parameter estimation of photovoltaic power systems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"104 ","pages":"Article 102359"},"PeriodicalIF":8.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147602319","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

Optimized Fuzzy Feedback Control for Autonomous Vehicle Trajectory Tracking via Adaptive Multi-Objective Differential Evolution 基于自适应多目标差分进化的自动驾驶车辆轨迹跟踪优化模糊反馈控制

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-04-01 DOI: 10.1016/j.swevo.2026.102365

Hongwei Wang, Liqiang Wang, Yu Zhang, Xijian Niu

{"title":"Optimized Fuzzy Feedback Control for Autonomous Vehicle Trajectory Tracking via Adaptive Multi-Objective Differential Evolution","authors":"Hongwei Wang, Liqiang Wang, Yu Zhang, Xijian Niu","doi":"10.1016/j.swevo.2026.102365","DOIUrl":"10.1016/j.swevo.2026.102365","url":null,"abstract":"<div><div>This paper proposes a robust fuzzy trajectory tracking control strategy with superior multi-objective performance for autonomous vehicles in the framework of differential evolution (DE) algorithm. First, a Takagi-Sugeno(T-S) fuzzy vehicle model with uncertainty and time-varying speed is established. A nonparallel distribution compensation (non-PDC) control method is proposed to allow inconsistencies between the membership functions(MFs) of the T-S fuzzy model and fuzzy controller. Whereafter, in order to optimize MFs of the robust fuzzy feedback H∞ controller, a novel multi-objective differential evolution (MODE) algorithm is designed to adaptively adjust two probability factors, implement crossover mutation, Pareto sort based on congestion distance, and combine and switch strategies based on feasibility. Additionally, it introduces variable-scale chaotic optimization search to enhance the accuracy and global exploration capability of algorithm, so as to acquire superior performance. Furthermore, a feedback compensation mechanism is integrated into the torque control allocation to concurrently evaluate rolling stability and energy efficiency. Finally, simulation results validate the effectiveness and superiority of the proposed control strategy under complex road conditions.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"104 ","pages":"Article 102365"},"PeriodicalIF":8.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147602785","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

HGA-MPPO: A unified heterogeneous graph attention and multi-policy PPO framework for AGV-assisted flexible job shop scheduling HGA-MPPO: agv辅助柔性作业车间调度的统一异构图关注和多策略PPO框架

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-03-01 Epub Date: 2026-02-18 DOI: 10.1016/j.swevo.2026.102331

Xiaoyu Yang , Yuyan Han , Yuting Wang , Yuhang Wang , Leilei Meng

{"title":"HGA-MPPO: A unified heterogeneous graph attention and multi-policy PPO framework for AGV-assisted flexible job shop scheduling","authors":"Xiaoyu Yang , Yuyan Han , Yuting Wang , Yuhang Wang , Leilei Meng","doi":"10.1016/j.swevo.2026.102331","DOIUrl":"10.1016/j.swevo.2026.102331","url":null,"abstract":"<div><div>In the context of rapidly evolving intelligent manufacturing systems, efficient coordination between production and intralogistics is crucial for productivity and flexibility. The flexible job shop scheduling problem with automated guided vehicles (FJSP-AGVs) poses a significant challenge due to tight coupling among operations, machines, and transportation resources, limiting the effectiveness of traditional heuristics and learning-based methods. To address this, we propose a multi-policy proximal policy optimization (MPPPO) unified framework featuring a heterogeneous graph attention-based encoder. By representing operations, machines, and AGVs as nodes in a heterogeneous graph with multiple relation types, the encoder captures high-dimensional interactions and global system dependencies. In addition, a composite reward mechanism that jointly considers makespan minimization, machine load balancing, and AGV utilization is designed to enhance solution stability and operational efficiency. Extensive experiments on generated instances and public benchmark datasets demonstrate that the proposed method consistently outperforms representative heuristic rules and state-of-the-art deep reinforcement learning approaches. The results highlight its potential for real-world deployment in large-scale intelligent manufacturing systems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"102 ","pages":"Article 102331"},"PeriodicalIF":8.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147423702","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

Q-learning-based multi-objective hyper-heuristic algorithm for multi-campus university course scheduling 基于q学习的多目标超启发式多校区大学课程调度算法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-03-01 Epub Date: 2026-02-19 DOI: 10.1016/j.swevo.2026.102338

Zizhuang Zhu , Yongquan Zhou , Guo Zhou , Qifang Luo , Huajuan Huang

{"title":"Q-learning-based multi-objective hyper-heuristic algorithm for multi-campus university course scheduling","authors":"Zizhuang Zhu , Yongquan Zhou , Guo Zhou , Qifang Luo , Huajuan Huang","doi":"10.1016/j.swevo.2026.102338","DOIUrl":"10.1016/j.swevo.2026.102338","url":null,"abstract":"<div><div>The University Course Scheduling Problem (UCSP) is a critical combinatorial optimization challenge, particularly in multi-campus universities where cross-campus teacher commuting and resource coordination introduce unique complexities. Existing studies primarily focus on single-campus scenarios and single-objective optimization, failing to address the trade-offs between teacher commuting time, classroom utilization, and teaching quality. This study proposes a multi-objective optimization model (MCUCSP) for multi-campus university course scheduling problem explicitly incorporating cross-campus teacher commuting time, the number of classrooms utilized, and a comprehensive class hour index as key objectives. To solve this model, a Q-learning-based multi-objective hyper-heuristic algorithm (MO-QL-HH) is developed, which integrates high-level Q-learning and ten low-level heuristics (four random operators for exploration and six greedy operators for exploitation). Additionally, efficient initial solution generation (via greedy prioritization of high-difficulty tasks and classroom reutilization) and encoding-decoding mechanisms are designed to ensure constraint satisfaction and optimization efficiency. Systematic experiments on 20 multi-campus instances (2–6 campuses, 100–500 course tasks) demonstrate that MO-QL-HH outperforms well-known algorithms (NSGA-II, MOEA/D, NSGA-III, SPEA2, MOEA/D-AAWNs, and HGTSA) in terms of hypervolume (HV), inverted generational distance (IGD), and coverage metric (C-metric). Ablation experiments validate the necessity of integrating random and greedy operators, while parameter optimization via the full factorial design further enhances the algorithm's performance. This work provides an effective solution for multi-campus course scheduling, balancing teacher well-being, resource efficiency, and teaching quality.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"102 ","pages":"Article 102338"},"PeriodicalIF":8.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147423745","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 two-stage adaptive variable neighborhood search approach for electric vehicle routing problem with fuzzy demand 模糊需求下电动汽车路径问题的两阶段自适应变邻域搜索方法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-03-01 Epub Date: 2026-02-16 DOI: 10.1016/j.swevo.2026.102332

Fang Han, Yuqin Zhu

{"title":"A two-stage adaptive variable neighborhood search approach for electric vehicle routing problem with fuzzy demand","authors":"Fang Han, Yuqin Zhu","doi":"10.1016/j.swevo.2026.102332","DOIUrl":"10.1016/j.swevo.2026.102332","url":null,"abstract":"<div><div>This paper addresses the electric vehicle routing problem under fuzzy demand (EVRPFD), which is widespread in real-world logistics scenarios. To solve it, a chance-constrained model is formulated based on credibility theory, aiming to minimize the total distance traveled by all vehicles. Subsequently, a two-stage adaptive variable neighborhood search (TAVNS) metaheuristic is proposed, which consists of a pre-optimization phase and a rescheduling phase. In the pre-optimization phase, a series of search mechanisms are designed to construct pre-optimized routes. Particularly, an adaptive shaking operator is developed to dynamically guide the algorithm’s search direction. In the rescheduling phase, a stochastic simulation method is adopted to identify route failures in the pre-optimized routes generated from the pre-optimization phase, and a reallocation strategy is then proposed to address these failures. Experimental results demonstrate that the proposed TAVNS outperforms its competitors significantly and updates the known optimal solutions for several cases. Additionally, a feasibility analysis focusing on route repair strategies validates that the proposed reallocation strategy can efficiently tackle route failures, and the additional costs generated are lower compared to those of the traditional repair strategy. Finally, by examining results derived from different preference values, this study explores how decision-makers’ varying attitudes impact delivery costs and concludes that the optimal delivery plan is achieved when they adopt a relatively conservative attitude.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"102 ","pages":"Article 102332"},"PeriodicalIF":8.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147423708","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 knowledge transfer-based membrane evolutionary algorithm for solving large-scale sorted waste collection problem with timeliness 基于知识转移的膜进化算法求解大规模时效性分类垃圾收集问题

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-03-01 Epub Date: 2026-02-27 DOI: 10.1016/j.swevo.2026.102345

Wenxue Zhang , Boquan Gao , Aldy Gunawan , Yunyun Niu , Jianhua Xiao

{"title":"A knowledge transfer-based membrane evolutionary algorithm for solving large-scale sorted waste collection problem with timeliness","authors":"Wenxue Zhang , Boquan Gao , Aldy Gunawan , Yunyun Niu , Jianhua Xiao","doi":"10.1016/j.swevo.2026.102345","DOIUrl":"10.1016/j.swevo.2026.102345","url":null,"abstract":"<div><div>The sorted collection of municipal solid waste has emerged as an effective waste management strategy due to varying timeliness requirements across different waste types, giving rise to the critical research challenge of timeliness-based waste collection. While existing algorithms primarily focus on small-scale versions of this problem, solving large-scale timeliness-based waste collection problems remains particularly challenging. To tackle this issue, this paper proposes a knowledge transfer-based membrane evolutionary algorithm. Specifically, the original problem and simplified problem are constructed in different membranes respectively, and the knowledge transfer learning mechanism is incorporated into the membrane evolutionary algorithm, enabling effective information exchange between the original problem membrane and the simplified problem membrane to efficiently obtain high-quality solutions. Extensive experiments on large-scale benchmark instances demonstrate the superiority and efficiency of the proposed algorithm compared with other state-of-the-art approaches. Furthermore, ablation experiments further confirm the effectiveness of both the membrane operation rules and the knowledge transfer mechanism.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"102 ","pages":"Article 102345"},"PeriodicalIF":8.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147423691","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