Swarm and Evolutionary Computation最新文献_第5页

An enhanced LSHADE-based algorithm for global and constrained optimization in applied mechanics and power flow problems 应用力学和潮流问题的全局约束优化改进lshade算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-09 DOI: 10.1016/j.swevo.2025.102032

Karam M. Sallam , Ibrahim Alrashdi , Reda Mohamed , Mohamed Abdel-Basset

{"title":"An enhanced LSHADE-based algorithm for global and constrained optimization in applied mechanics and power flow problems","authors":"Karam M. Sallam , Ibrahim Alrashdi , Reda Mohamed , Mohamed Abdel-Basset","doi":"10.1016/j.swevo.2025.102032","DOIUrl":"10.1016/j.swevo.2025.102032","url":null,"abstract":"<div><div>This study proposes a new evolutionary algorithm, namely NL-SHADE, that combines the linear population size reduction-based SHADE (L-SHADE) with the Nutcracker Optimization Algorithm (NOA) to better solve global optimization and real-world constrained optimization problems. Several optimization algorithms have been developed in the literature to address these issues. However, they still stall in local optima and exhibit slow convergence speed, which are the main limitations that motivate us to propose the NL-SHADE algorithm. In this algorithm, the SHADE algorithm is responsible for the exploration operator in the early stages of the optimization process to avoid stagnation in local optima, while NOA is responsible for improving convergence speed. Furthermore, at the end of each generation, the linear population size reduction method is used to exclude some inferior solutions that might lead to local optima and reduce convergence speed. To solve the constrained optimization problems, NL-SHADE is combined with a gradient-based repair method to propose a new variant, rNL-SHADE, which uses gradient information from the constraint set to direct infeasible solutions into feasible regions. In this study, two experiments are conducted. In the first experiment, the proposed NL-SHADE is evaluated using two unconstrained CEC benchmarks, CEC2017 and CEC2020, and compared with numerous cutting-edge algorithms using several performance metrics. In the second experiment, the performance of the proposed algorithms is also tested by solving 29 RWC optimization problems from four different domains. The experimental findings demonstrate that, for the majority of the solved RWC problems, rNL-SHADE can perform better than all compared algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102032"},"PeriodicalIF":8.2,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241383","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 instance sampling for multi-objective automatic algorithm configuration 动态实例采样的多目标自动算法配置

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-08 DOI: 10.1016/j.swevo.2025.102008

Yuchen Li, Handing Wang

{"title":"Dynamic instance sampling for multi-objective automatic algorithm configuration","authors":"Yuchen Li, Handing Wang","doi":"10.1016/j.swevo.2025.102008","DOIUrl":"10.1016/j.swevo.2025.102008","url":null,"abstract":"<div><div>Multi-objective automatic algorithm configuration alleviates the tedious parameter tuning for users by optimizing both the performance and efficiency of the target algorithm. Its evaluation requires performing multiple runs for each configuration on an instance set, making the computational cost expensive. Especially for real-world application problems, it is crucial to reduce computational costs under limited budgets. However, when the instance set is large, model-based approaches struggle to further reduce the high cost of configuration evaluations, which remains a significant challenge. To address this, we propose a Kriging-assisted Two_Arch2 with dynamic instance sampling algorithm, which aims to reduce the high costs of configuration evaluations by lowering the fidelity of the evaluations. Specifically, we align the number of evaluation instances with the evaluation fidelity and design a dynamic instance sampling strategy to effectively control the frequency of new instance sampling, enabling fidelity switching. Moreover, a trade-off configuration selection method is proposed to assist users in choosing configurations when preferences are unclear. The proposed method has been tested on numerous instances from the BBOB benchmark suite. The experimental results demonstrate that the performance of the proposed algorithm outperforms other state-of-the-art methods.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102008"},"PeriodicalIF":8.2,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241381","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

Train energy-efficient speed profile generation under various track alignments: a quantum evolutionary algorithm with cosine annealing 不同轨道对准下列车节能速度线生成：余弦退火的量子进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-07 DOI: 10.1016/j.swevo.2025.102027

Yang Qin , Jingwei Guo , Peijuan Xu , Duo Li , Yonggang Wang

{"title":"Train energy-efficient speed profile generation under various track alignments: a quantum evolutionary algorithm with cosine annealing","authors":"Yang Qin , Jingwei Guo , Peijuan Xu , Duo Li , Yonggang Wang","doi":"10.1016/j.swevo.2025.102027","DOIUrl":"10.1016/j.swevo.2025.102027","url":null,"abstract":"<div><div>The optimization of train speed profiles is an effective method for reducing energy consumption and operating costs for urban rail transit (URT). In recent years, numerous intelligent optimization algorithms have been employed to generate high-quality solutions for speed profile optimization models. However, most previous studies preset the train operation sequence, overlooking the complex impacts of track alignment diversity on train operation modes. This limitation hinders the applicability of these findings across multiple inter-stations within the URT network. Moreover, there has been insufficient validation of the proposed optimization algorithms under diverse train operation scenarios. This study reformulates the generation of train speed profiles as a multi-stage decision-making problem within a continuous speed space. By applying the law of conservation of energy, a dynamic model of the train operation process is established and the decision evolution of the train speed is mapped into a high-dimensional quantum space. Subsequently, an improved quantum evolutionary algorithm with adaptive rotation strategies is designed using the cosine annealing function (CA-QEA). Furthermore, a conditional quantum collapse mechanism is introduced to enhance the global search capability of the algorithm. Utilizing actual URT data from Tianjin, China, three test scenarios featuring various track alignments, including gentle slopes, energy-saving slopes, and multiple continuous steep slopes, are constructed. The results demonstrate that the proposed method can effectively reduce the traction energy consumption (TEC) in three different track alignment. The Friedman non-parametric test results indicate that the performance of CA-QEA surpasses that of several mature intelligent optimization algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102027"},"PeriodicalIF":8.2,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231890","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

Front guided surrogate-assisted evolutionary algorithm with adaptive selection of three-branch infill criteria for expensive many-objective optimization 基于自适应选择三分支填充准则的前沿引导代理辅助进化算法用于昂贵的多目标优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-07 DOI: 10.1016/j.swevo.2025.101982

Yi Jiao Xu , Sheng Xin Zhang , Jie Lin , Shao Yong Zheng , Xian Hua Dai

{"title":"Front guided surrogate-assisted evolutionary algorithm with adaptive selection of three-branch infill criteria for expensive many-objective optimization","authors":"Yi Jiao Xu , Sheng Xin Zhang , Jie Lin , Shao Yong Zheng , Xian Hua Dai","doi":"10.1016/j.swevo.2025.101982","DOIUrl":"10.1016/j.swevo.2025.101982","url":null,"abstract":"<div><div>Expensive optimization problems are those where a single function evaluation takes a large amount of time, and are typically only allowed for a finite number of function evaluations. Thus, algorithms are extremely limited in the computational resources, and need to balance their ability of promoting convergence with of maintaining diversity. Especially when coping with many-objective optimization problems where the conflict between the two demands is further exacerbated, adaptive strategies that can promptly adjust computational resources according to the state of the population are of particular importance. To effectively solve expensive many-objective optimization problems with different characteristics, we propose a front guided surrogate-assisted evolutionary algorithm with adaptive selection of three-branch infill criteria (FGSAEA). FGSAEA consists of the front guided adaptive selection strategy and three infill criteria, all of which are featured by the positional relationship between the front of the candidate population and the archive storing truly evaluated solutions. FGSAEA uses the front guided adaptive selection strategy to determine the evolutionary state. To reduce the interference from inaccurate predictions of surrogate models, the strategy always takes the front of an archive storing all evaluated solutions as a reference. Then, FGSAEA utilizes one of three infill criteria respectively focused on promoting the convergence, the diversity of population, or the accuracy of surrogate models according to the demands. Experiments on benchmark problems demonstrate that FGSAEA is very competitive compared to state-of-the-art surrogate-assisted expensive many-objective optimization algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 101982"},"PeriodicalIF":8.2,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231889","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

Surrogate-assisted evolutionary algorithms for a bilevel location and latency-oriented routing problem 面向二层定位和延迟的路由问题的代理辅助进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-07 DOI: 10.1016/j.swevo.2025.102005

Jesús-Adolfo Mejía-de-Dios , José-Fernando Camacho-Vallejo , Rosa G. González-Ramírez

{"title":"Surrogate-assisted evolutionary algorithms for a bilevel location and latency-oriented routing problem","authors":"Jesús-Adolfo Mejía-de-Dios , José-Fernando Camacho-Vallejo , Rosa G. González-Ramírez","doi":"10.1016/j.swevo.2025.102005","DOIUrl":"10.1016/j.swevo.2025.102005","url":null,"abstract":"<div><div>Hierarchies among different stakeholders within a supply chain are common and should not be overlooked. In this study, we address both location and routing decisions within the supply chain framework. Specifically, we focus on a problem inspired by a real-life situation involving two stakeholders: one (the leader) responsible for determining the location and size of depots, and another (the follower) responsible for delivering products to customers. The leader aims to minimize costs, while the follower seeks to minimize latency, which is interpreted as the waiting time of customers along the routes. To address this hierarchical situation, we propose a novel bilevel optimization model. The complexity of this model, which includes both binary and continuous variables at each level and features high dimensionality due to a multi-level network accounting for modeling customers’ latency, precludes the use of a single-level reformulation. Therefore, we propose an evolutionary algorithm to solve the bilevel problem. Given the challenging nature of the follower’s problem, a classical nested approach would be excessively time-consuming. Thus, we employ surrogate methods to approximate the latency-oriented routing decision process, integrating them into the evolutionary algorithm’s framework. This approach provides an effective means of addressing the complexities while maintaining the feasibility of the bilevel solutions. The surrogate strategy is based on a committee of learning models trained on limited data from bilevel feasible solutions. Several variants are studied and compared against state-of-the-art surrogate algorithms, obtaining better results with less computational time for the problem under study.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102005"},"PeriodicalIF":8.2,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231888","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 metaheuristic algorithm for the multi-drops flying sidekick traveling salesman problem 多滴飞伴旅行商问题的两阶段元启发式算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-06 DOI: 10.1016/j.swevo.2025.102001

Sheng-Zong Chen , Ren-Yong Guo

{"title":"A two-stage metaheuristic algorithm for the multi-drops flying sidekick traveling salesman problem","authors":"Sheng-Zong Chen , Ren-Yong Guo","doi":"10.1016/j.swevo.2025.102001","DOIUrl":"10.1016/j.swevo.2025.102001","url":null,"abstract":"<div><div>In this paper, an integer programming model, based on the flight range limitation of the drone, is formulated for the multi-drops flying sidekick traveling salesman problem (mFSTSP). The connection between the mFSTSP and the corresponding traveling salesman problem (TSP) is then explored, providing a basis for solving the problem in two stages. Subsequently, a new two-stage metaheuristic algorithm is proposed. In the first stage, an adaptive large neighborhood search algorithm with the nearest neighbor operator is employed to solve the corresponding TSP. In the second stage, the obtained TSP route is segmented based on the flight range limitation of the drone, and the simulated annealing framework is used to explore the optimal node allocation scheme of each segment in sequence. Numerical experiments are conducted under varying truck-drone speed ratios and diverse drone maximum flight ranges. The experimental results indicate that optimal or near-optimal solutions to the problem can be obtained in a significantly short time. Furthermore, the proposed two-stage metaheuristic algorithm shows remarkable advantages in solving large-scale instances compared with several advanced heuristic algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102001"},"PeriodicalIF":8.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231887","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

Discrete adaptive GWO-based transport scheduling for aircraft between spots on flight deck and hangar 基于离散自适应gwo的飞机甲板与机库间运输调度

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-06 DOI: 10.1016/j.swevo.2025.102029

Xichao Su , Zixuan Liu , Xiaohua Han , Yu Wu , Rongwei Cui , Xuan Li

{"title":"Discrete adaptive GWO-based transport scheduling for aircraft between spots on flight deck and hangar","authors":"Xichao Su , Zixuan Liu , Xiaohua Han , Yu Wu , Rongwei Cui , Xuan Li","doi":"10.1016/j.swevo.2025.102029","DOIUrl":"10.1016/j.swevo.2025.102029","url":null,"abstract":"<div><div>Transport scheduling of carrier-based aircraft on flight deck and hangar is an important means of improving the operational efficiency of carrier-aircraft system. In this paper, an improved grey wolf algorithm-based scheduling method is proposed to address the issues of transport scheduling of carrier-based aircraft between spots on flight-deck and hangar. First, a transport path planning algorithm, which combines the improved A* algorithm and the optimal control algorithm is proposed to generate the transport route library between the flight deck and the hangar parking spots. Second, based on optimization objectives such as transport completion time, load balancing of transport groups, and transport time of tractors, as well as constraints on the time, space and resource transfer during the transport process, the mathematical model for transport scheduling is established. Then, a discrete adaptive grey wolf optimization (DAGWO) algorithm is designed to solve the model, in which the strategies of discretizing the optimization variables, setting of pre-constraint, improving parameter are integrated, and global leader wolf strategy, joint mutation, and local restructuring mechanism are also introduced in this algorithm. The effectiveness of the model and the performance of the DAGWO algorithm are verified through simulations and comparisons under multiple missions with different transport scale.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102029"},"PeriodicalIF":8.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231886","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

Probing the fitness landscape of the influential nodes for the influence maximization problem in social networks 探讨社交网络中影响最大化问题的影响节点的适应度景观

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-05 DOI: 10.1016/j.swevo.2025.102002

Jianxin Tang, Jiaqiang Fu, Xinyue Li, Lele Geng, Juan Pang

{"title":"Probing the fitness landscape of the influential nodes for the influence maximization problem in social networks","authors":"Jianxin Tang, Jiaqiang Fu, Xinyue Li, Lele Geng, Juan Pang","doi":"10.1016/j.swevo.2025.102002","DOIUrl":"10.1016/j.swevo.2025.102002","url":null,"abstract":"<div><div>Influence Maximization (IM) is a key issue of information dissemination and has been proved to be an NP-hard problem. However, traditional methods always suffer from low efficiency, poor scalability, and tend to fall into local optima. Probing the promising distribution regions of the potential influential nodes from the macroscopic perspective is necessary and helpful in understanding the influence propagation. To address such challenges, this paper makes attempt to depict the fitness landscape distribution of the expected influence of the social individuals in the network from a novel perspective. An entropy measure is introduced as a decision criterion and a fitness landscape-guided differential evolution optimization (FLDE) is proposed. Firstly, the distribution of the potential solution regions is depicted by characterizing the fitness landscape designed specially for IM problem. Next, a guiding strategy based on the fitness landscape is conceived to drive the differential evolution towards more promising solution regions by avoiding the entrapment in local optima. Experiments conducted on six real social networks and three synthetic networks indicate that the FLDE outperforms the state-of-the-art baselines by an average of 16% in influence spread and shows strong scalability when dealing with different types of networks.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102002"},"PeriodicalIF":8.2,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212125","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

Boosting classification accuracy using an efficient stochastic optimization technique for feature selection in high-dimensional data 利用高效的随机优化技术提高高维数据的分类精度

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-05 DOI: 10.1016/j.swevo.2025.102025

Noureen Talpur , Shoaib-ul Hassan , Mohd Hafizul Afifi Abdullah , Abdulrahman Aminu Ghali , Ambreen Abdul Raheem , Shazia Khatoon , Norshakirah Aziz , Sivashankari Alaganandham

{"title":"Boosting classification accuracy using an efficient stochastic optimization technique for feature selection in high-dimensional data","authors":"Noureen Talpur , Shoaib-ul Hassan , Mohd Hafizul Afifi Abdullah , Abdulrahman Aminu Ghali , Ambreen Abdul Raheem , Shazia Khatoon , Norshakirah Aziz , Sivashankari Alaganandham","doi":"10.1016/j.swevo.2025.102025","DOIUrl":"10.1016/j.swevo.2025.102025","url":null,"abstract":"<div><div>Many real-world problems involve a large number of features, among which several features are irrelevant or redundant. This problem not only increases the dimensionality but also reduces the classification performance of machine learning models. To address this issue, feature selection methods have been extensively used in the literature, either by applying existing algorithms or developing new algorithms. However, many of these approaches suffer from limitations such as insufficient feature reduction due to getting trapped in local minima in the large search space. Hence, this study proposed a recent stochastic optimization-based technique called the Osprey Optimization Algorithm (OOA). The OOA algorithm has the capability of balancing exploration and exploitation effectively during the search process, making it suitable for solving high-dimensional optimization tasks. To validate the efficiency of the selected feature subsets, the study employs the <em>k</em>-nearest neighbor (<em>k</em>-NN) classifier. Comparative results between OOA and five state-of-the-art algorithms show that OOA achieves the highest average classification accuracy of 89.22 %, while selecting the fewest average features of 70.63 and reduces the feature burden by 62.80 %. Moreover, the results of a non-parametric Wilcoxon signed-rank test based on classification accuracy show a <em>p</em>-value less than 5.00E-02, confirming a statistically significant difference in performance among the six algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102025"},"PeriodicalIF":8.2,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144222795","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 matheuristic-based self-learning approach for distributed heterogeneous assembly flowshop scheduling with multiple assembly factories and make-to-order delivery 基于数学的分布式异构装配流程车间调度和按订单交付的自学习方法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-05 DOI: 10.1016/j.swevo.2025.101996

Zikai Zhang , Shujun Yu , Qiuhua Tang , Liping Zhang , Zixiang Li , Lixin Cheng , Yingli Li

{"title":"A matheuristic-based self-learning approach for distributed heterogeneous assembly flowshop scheduling with multiple assembly factories and make-to-order delivery","authors":"Zikai Zhang , Shujun Yu , Qiuhua Tang , Liping Zhang , Zixiang Li , Lixin Cheng , Yingli Li","doi":"10.1016/j.swevo.2025.101996","DOIUrl":"10.1016/j.swevo.2025.101996","url":null,"abstract":"<div><div>Concerns about mass personalized customization and customer services have highlighted the importance of make-to-order delivery in distributed manufacturing areas. These make-to-order delivery services are deeply intertwined with distributed assembly scheduling, where variations in customer demand significantly influence production costs and efficiency. To address this, we propose the distributed heterogeneous assembly flowshop scheduling with multiple assembly factories and make-to-order delivery. Our approach begins with a mixed-integer linear programming model aimed at minimizing the tardiness cost. Subsequently, a hybrid algorithm, incorporating mathematical programming, iterated greedy technique, and self-learning strategy, is designed to solve the model, and termed matheuristic-based self-learning iterated greedy (MSIG) algorithm. This algorithm features a matheuristic-based decoding mechanism and a problem-specific NEH heuristic to generate high-quality initial solution. The nested greedy phase involves the extraction of both customers and products to refine solution quality. Furthermore, the local search phase incorporates knowledge-based operators, rule-based operator candidate sets, and a self-learning selection strategy to enhance the algorithm’s exploratory capabilities. Finally, through comprehensive comparisons with nine existing heuristics and six state-of-the-art meta-heuristics, the superiority of the MSIG algorithm and the efficacy of its components are conclusively demonstrated.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 101996"},"PeriodicalIF":8.2,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144222796","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