Swarm and Evolutionary Computation最新文献_第9页

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

A hybrid surrogate co-assisted evolutionary algorithm with prediction fusion interpolation sampling strategy for expensive optimization problems 基于预测融合插值采样策略的混合代理协同辅助进化算法求解昂贵优化问题

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-03 DOI: 10.1016/j.swevo.2025.102003

Zihe Shi , Qinghua Su , Zhongbo Hu , Gang Huang

{"title":"A hybrid surrogate co-assisted evolutionary algorithm with prediction fusion interpolation sampling strategy for expensive optimization problems","authors":"Zihe Shi , Qinghua Su , Zhongbo Hu , Gang Huang","doi":"10.1016/j.swevo.2025.102003","DOIUrl":"10.1016/j.swevo.2025.102003","url":null,"abstract":"<div><div>Hybrid surrogate-assisted evolutionary algorithms, which achieve the purpose of assisting search by hybridizing local and global surrogate models, are a kind of competitive state-of-the-art techniques for solving expensive optimization problems (EOPs). The sampling points of the local models have been introduced but are failed to investigate directly in this field. In fact, sampling points are one of important determinants of model performance. Unlike the existing technologies including superior individuals sampling and neighboring individuals sampling, this paper develops a prediction fusion interpolation sampling strategy (PFs) and proposes a hybrid surrogate co-assisted evolutionary algorithm with it (HSCEAwP). The presented PFs applies all the best predictions of the local and global models of all historical populations as the sampling points of the next local surrogate model. The proposed HSCEAwP inherits the optimization framework of the generalized multifactorial evolutionary algorithm. The radial basis function model is chosen as the modeling basis of the local and global surrogate models. The performance of PFs under radial basis function model is analyzed theoretically and experimentally based on the interpolation principle. The performance of HSCEAwP is tested on eight common benchmark problems, ten CEC2017 composition problems and an electrostatic precipitator optimization problem. The experimental results demonstrate more reliable performance of HSCEAwP to well-established algorithms in terms of solving accuracy.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 102003"},"PeriodicalIF":8.2,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203766","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

Data-driven assisted multiobjective routing optimization for multimodal transportation under fuzzy demand 模糊需求下多式联运数据驱动辅助多目标路径优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-03 DOI: 10.1016/j.swevo.2025.101997

Jinlong Zhou , Yinggui Zhang , Juan Wang , Yang Xiao , Yuhan Wang , Xupeng Wen , Lining Xing

{"title":"Data-driven assisted multiobjective routing optimization for multimodal transportation under fuzzy demand","authors":"Jinlong Zhou , Yinggui Zhang , Juan Wang , Yang Xiao , Yuhan Wang , Xupeng Wen , Lining Xing","doi":"10.1016/j.swevo.2025.101997","DOIUrl":"10.1016/j.swevo.2025.101997","url":null,"abstract":"<div><div>Multimodal transportation, as an efficient and comprehensive transport mode in the modern logistics system, significantly improves logistics efficiency and reduces costs by integrating various transportation modes. However, due to uncertainties such as weather conditions, logistics companies cannot accurately grasp and predict freight transportation demand. Additionally, the time-sensitivity requirements of multimodal transportation frequently lead to delays in freight transportation. To characterize the randomness and uncertainty of transportation demand parameters, triangular fuzzy numbers are introduced as a descriptive tool. A multiobjective optimization model for low-carbon multimodal transport routing under uncertain demand is constructed, aiming to minimize total transportation cost and carbon emissions. Monte Carlo simulation combined with data-driven methods is employed to sample and analyze uncertain demand, effectively addressing demand uncertainty. To solve the developed model, a novel multi-form competitive swarm optimization algorithm is proposed, which enhances both the quality and efficiency of the solutions. The experimental results indicate that compared to peer algorithms, the proposed algorithm achieves a better tradeoff in diversity, convergence, and feasibility, ultimately obtaining superior performance.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101997"},"PeriodicalIF":8.2,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203750","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 heterogeneous sparsity knowledge guided evolutionary algorithm for sparse large-scale multiobjective optimization 基于异构稀疏知识的大规模稀疏多目标优化进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-03 DOI: 10.1016/j.swevo.2025.102000

Jing Jiang , Huoyuan Wang , Pingping Tong , Juanjuan Hong , Zhe Liu , Xing Zhuang , Benyue Su , Fei Han

{"title":"A heterogeneous sparsity knowledge guided evolutionary algorithm for sparse large-scale multiobjective optimization","authors":"Jing Jiang , Huoyuan Wang , Pingping Tong , Juanjuan Hong , Zhe Liu , Xing Zhuang , Benyue Su , Fei Han","doi":"10.1016/j.swevo.2025.102000","DOIUrl":"10.1016/j.swevo.2025.102000","url":null,"abstract":"<div><div>Research on sparse large-scale multiobjective optimization problems (LSMOPs) is rapidly growing due to their diverse applications in science and engineering. Existing studies typically employ static or dynamic knowledge to guide the search in evolutionary algorithms for solving sparse LSMOPs. However, relying solely on a single type of sparsity knowledge may result in ambiguous guidance and suboptimal optimization. To address this, we propose an evolutionary algorithm based on heterogeneous sparsity knowledge (HSKEA). In this approach, static knowledge is represented by a scoring vector to assess the importance of each decision variable, while dynamic knowledge is captured by indicator vectors that identify whether a decision variable is zero or non-zero and iteratively updates based on the population distribution. Two types of populations, including main and auxiliary populations, are initialized using both dynamic and static knowledge and evolved through a new genetic operator guided by heterogeneous sparsity knowledge and information sharing. Experimental results comparing HSKEA to four state-of-the-art algorithms across eight benchmark test problems and three real-world scenarios demonstrate its effectiveness.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 102000"},"PeriodicalIF":8.2,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203749","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

Diversity-enhanced hyper-heuristics for multi-objective dynamic flexible job shop scheduling 多目标动态柔性作业车间调度的多样性增强超启发式算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-02 DOI: 10.1016/j.swevo.2025.101994

Yuan Shi , Yaoming Yang , Bingdong Li , Hong Qian , Hao Hao , Aimin Zhou

引用次数: 0