Swarm and Evolutionary Computation最新文献_第7页

PF-MAAC: A learning-based method for probabilistic optimization in time-constrained non-adversarial moving target search

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101785

Qihang Peng , Hongliang Guo , Zhengyan Zhang , Chih-Yung Wen , Yaochu Jin

{"title":"PF-MAAC: A learning-based method for probabilistic optimization in time-constrained non-adversarial moving target search","authors":"Qihang Peng , Hongliang Guo , Zhengyan Zhang , Chih-Yung Wen , Yaochu Jin","doi":"10.1016/j.swevo.2024.101785","DOIUrl":"10.1016/j.swevo.2024.101785","url":null,"abstract":"<div><div>This paper investigates the multi-robot efficient search (MuRES) problem with a focus on maximizing the probability of capturing a moving target within a predefined time constraint. Given the complexity of the MuRES problem, traditional optimization algorithms often result in significant computational overhead. As a result, learning-assisted intelligent optimization, particularly reinforcement learning (RL), has emerged as a prominent research trend, providing more efficient and adaptive solutions. However, the non-additive nature of the objective to maximize capture probability complicates the direct application of canonical RL-based algorithms. To address the challenge, we propose the probabilistically factorized multi-agent actor-critic (PF-MAAC) algorithm, which serves as a lightweight solution aligned with probability theory specifically designed to handle the complexities of the maximal capture probability objective. PF-MAAC is composed of (1) a generalized temporal difference (GTD) module to establish the temporal-difference relationship of the central value function, (2) a probability-based factorization (P-FAC) module to decompose the central value function into individual ones in a probability-compliant manner, and (3) an extended policy gradient (EPG) module which updates each robot’s actor-network based on the decomposed individual value function. Comparative simulations across various MuRES test environments demonstrate that PF-MAAC outperforms state-of-the-art methods. Furthermore, we successfully deployed PF-MAAC in a real multi-robot system for moving target search in a self-constructed indoor environment, achieving the satisfactory results for different time constraints.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101785"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183506","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

Pareto multi-objective optimization for high locality-preserving space-filling curve identification

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101797

Patrick Franco, Rémy Mullot, Valentin Owczarek

{"title":"Pareto multi-objective optimization for high locality-preserving space-filling curve identification","authors":"Patrick Franco, Rémy Mullot, Valentin Owczarek","doi":"10.1016/j.swevo.2024.101797","DOIUrl":"10.1016/j.swevo.2024.101797","url":null,"abstract":"<div><div>Space-filling curves are widely used in applications that take advantage of the locality-preserving property. The pattern (the first order curve) plays a central role in locality preserving property. This was highlighted in previous works well-received by the community. A formulation was established leading to defined new patterns, including competitive patterns, i.e., patterns carrying comparable (and sometimes better) locality-preserving level than the Hilbert curve, so far the reference. Nevertheless, the number of pattern solutions resulting from the given formulation exponentially grows up with the space dimension.</div><div>In this article, with the help of an evolutionary algorithm, an original approach dedicated to the identification of high locality-preserving multidimensional patterns is proposed. Our idea is to embed the problem in a multi-objective optimization framework guided by Pareto optimality. In a such framework, each locality score (through a standard criteria) obtained by a pattern at a specific radius value can be processed as an objective function (to be minimized). The overall multi-objective function then illustrates how well the objectives are met, i.e. how the locality is achieved as progressively the space is filled. So, several space radii of interest are taken into account in the locality estimation and not a single one. This track contributes to define an accurately process of pattern identification.</div><div>Comparative experimental results led on dimensions upper than three, seem to confirm that the proposed approach is <em>Reliable</em>, <em>Efficient</em> and <em>Flexible</em>. The results showed that the classical RBG pattern is not Pareto optimal in the 5-D case and alternative patterns are emerging. Finally, being able to identify patterns that preserve locality at a competitive level compare to the referent Hilbert curve (RBG pattern-based) constitutes a real contribution and could greatly improve the effectiveness of applications.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101797"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183498","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

Goal-directed multimodal multi-objective evolutionary algorithm converging on population derivation

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101796

Shaobo Deng , Hangyu Liu , Kaixin Cheng , Jinyu Xu , Min Li , Hua Rao

{"title":"Goal-directed multimodal multi-objective evolutionary algorithm converging on population derivation","authors":"Shaobo Deng , Hangyu Liu , Kaixin Cheng , Jinyu Xu , Min Li , Hua Rao","doi":"10.1016/j.swevo.2024.101796","DOIUrl":"10.1016/j.swevo.2024.101796","url":null,"abstract":"<div><div>Recently, multimodal multi-objective problems (MMOPs) have become a popular research field in multi-objective optimization problems. The key to solving MMOPs lies in finding multiple equivalent Pareto sets (PSs) corresponding to the Pareto front (PF). Therefore, while balancing the convergence and diversity of the algorithm, it is crucial to enhance its search ability in the decision space. Current research mainly focuses on identifying solutions with exploratory potential, retaining their advantages during evolution, thereby increasing the chances of finding more equivalent PSs. However, these potential solutions and the resulting high-quality solutions are often scarce and require multiple iterations to effectively explore their space. Based on this, this paper proposes a goal-directed multimodal multi-objective evolutionary algorithm converging on population derivation, which includes three stages: population derivation, diversity maintenance, and convergence. In the population derivation stage, the algorithm identifies individuals with exploratory potential and derives more individuals in their subspaces to facilitate more efficient exploration of these subspaces. The diversity maintenance stage balances the population's distribution in both the decision and objective spaces, while the convergence stage accelerates the population's approach to the true PF. These three stages work synergistically under their respective objectives to optimize the distribution of solution sets in both the objective and decision spaces and to obtain the complete set of equivalent Pareto solutions. Experimental results show that this algorithm outperforms several mainstream algorithms on multiple MMOP test sets.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101796"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183502","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 multiple surrogate-assisted hybrid evolutionary feature selection algorithm

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101809

Wan-qiu Zhang , Ying Hu , Yong Zhang , Zi-wang Zheng , Chao Peng , Xianfang Song , Dunwei Gong

{"title":"A multiple surrogate-assisted hybrid evolutionary feature selection algorithm","authors":"Wan-qiu Zhang , Ying Hu , Yong Zhang , Zi-wang Zheng , Chao Peng , Xianfang Song , Dunwei Gong","doi":"10.1016/j.swevo.2024.101809","DOIUrl":"10.1016/j.swevo.2024.101809","url":null,"abstract":"<div><div>Feature selection (FS) is an important data processing technology. However, existing FS methods based on evolutionary computation have still the problems of “curse of dimensionality” and high computational cost, with the increase of the number of feature and/or the size of instance. In view of this, the paper proposes a multiple surrogate-assisted hybrid evolutionary feature selection (MSa-HEFS). Two kinds of surrogates (i.e., objective regression surrogate and sample surrogate) and two kinds of FS methods (i.e., filter and wrapper) are integrated into MSa-HEFS to improve its performance. Firstly, an ensemble filter FS method is designed to reduce the search space of subsequent wrapper evolutionary FS method. Secondly, in the proposed evolutionary FS method, a dual-surrogate-assisted hierarchical individual evaluation mechanism is developed to reduce the evaluation cost on feature subsets, an online management and update strategy is used to adaptively choose appropriate surrogates for individuals. The proposed algorithm is applied to 12 typical datasets and compared with 4 state-of-the-art FS algorithms. Experimental results show that MSa-HEFS can obtain good feature subsets at the smallest computational cost on all datasets. MSa-HEFS source code is available on Github at <span><span>https://github.com/ZZW-zq/MSa-HEFS-/tree/master</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101809"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183600","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

Structural bias in metaheuristic algorithms: Insights, open problems, and future prospects

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101812

Kanchan Rajwar , Kusum Deep

{"title":"Structural bias in metaheuristic algorithms: Insights, open problems, and future prospects","authors":"Kanchan Rajwar , Kusum Deep","doi":"10.1016/j.swevo.2024.101812","DOIUrl":"10.1016/j.swevo.2024.101812","url":null,"abstract":"<div><div>This paper addresses a critical issue of structural bias in metaheuristic algorithms, a key factor that often hinders their effectiveness in solving complex optimization problems. Such biases, typically resulting from the design of algorithmic operators and solution construction processes, can lead to a decrease in performance over time. Despite its importance, structural bias is little understood and rarely explored. Moreover, the theoretical framework for structural bias in this context is notably underdeveloped. To the best of our knowledge, no comprehensive review of structural bias in metaheuristic algorithms is available to date. Consequently, this study is subjected to a thorough literature review, providing the mathematical definition of structural bias, the theoretical background, and an extensive analysis of its various forms within metaheuristic algorithms. This paper discusses structural bias in several metaheuristic algorithms, including the Genetic Algorithm, Particle Swarm Optimization, Differential Evolution, and Ant Colony Optimization. Methodologies for identifying structural bias, currently scattered across several studies, are categorized into four classes and discussed through the implementation of Particle Swarm Optimization, highlighting their advantages and limitations. Additionally, five critical open problems are identified, and essential research directions for future exploration are outlined. As the first comprehensive review of structural bias – an issue gaining increasing attention – this work is expected to serve as a vital resource for algorithm designers and the research community.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101812"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183500","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

Deep reinforcement learning assisted surrogate model management for expensive constrained multi-objective optimization

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101817

Shuai Shao, Ye Tian, Yajie Zhang

{"title":"Deep reinforcement learning assisted surrogate model management for expensive constrained multi-objective optimization","authors":"Shuai Shao, Ye Tian, Yajie Zhang","doi":"10.1016/j.swevo.2024.101817","DOIUrl":"10.1016/j.swevo.2024.101817","url":null,"abstract":"<div><div>Expensive constrained multi-objective optimization problems (ECMOPs) exist in a wide variety of applications from industrial processes to engineering systems. When solving ECMOPs, with only a limited number of function evaluations available, a common approach is to substitute the real function evaluations with more affordable evaluations provided by computationally efficient surrogate models. However, existing surrogate assisted evolutionary algorithms (SAEAs) exhibit poor versatility in handling various ECMOPs, as they only use a constant surrogate modeling scheme or switch the modeling schemes with expert knowledge. To address the dilemma in surrogate modeling, this paper proposes a deep reinforcement learning assisted evolutionary algorithm, which operates on two key issues. First, multiple surrogate models are employed to learn the approximate function of an ECMOP using previously evaluated solutions during the evolutionary process. Second, a deep reinforcement learning method is employed to learn the optimal surrogate model management strategy based on evolutionary experience, selecting the most suitable surrogate modeling scheme for the current generation. Experimental evaluations on a large number of expensive problems demonstrate that the proposed algorithm has a significant effect compared with state-of-the-art competitors.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101817"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183505","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 decompose-and-learn multi-objective algorithm for scheduling large-scale earth observation satellites

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101792

Jing Qi , Min Hu , Lining Xing

{"title":"A decompose-and-learn multi-objective algorithm for scheduling large-scale earth observation satellites","authors":"Jing Qi , Min Hu , Lining Xing","doi":"10.1016/j.swevo.2024.101792","DOIUrl":"10.1016/j.swevo.2024.101792","url":null,"abstract":"<div><div>In recent studies, task scheduling problems of earth observation satellites (EOSs) still encounter large difficulties when they meet the increasing number of satellites. Moreover, multiple objectives and increasing number of tasks must be considered in business affairs. To effectively address these issues, considering satellite orbits as independent resources, a multi-objective algorithm is tailored for earth observation satellites scheduling problems (EOSSPs) in this paper. The algorithm includes a novel dynamic learning task allocation mechanism and a bidirectional sorting strategy with global patching method. Specifically, the mechanism works as an evolution operator to allocate tasks to appropriate orbits thus reducing problem complexity and the strategy is tailored to schedule observation windows to corresponding tasks. With the mechanism, original EOSSP can be decomposed into subproblems through the task allocation mechanism, with each task assigned to an corresponding orbit using adaptively updating guideline values. The decision space of a subproblem is limited within one single orbit, which will vastly decrease the complexity of the original large-scale EOSSP. Then, the bidirectional sorting strategy will schedule specific observation windows of each orbit to the candidate tasks. Since the algorithm is proposed to solve large-scale EOSSPs with multi-orbit and multi-objective, it is referred here as LS2MO-SS. Finally, extensive experiments are conducted on ten large-scale problems with different tasks to evaluate the performance of the multi-objective algorithm, the proposed evolution operator, and the bidirectional sorting strategy. The comparative results indeed validate the effectiveness of the proposed algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101792"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183609","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 Nonlinear Dimensionality Reduction Search Improved Differential Evolution for large-scale optimization

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101832

Yifei Yang , Haotian Li , Zhenyu Lei , Haichuan Yang , Jian Wang

{"title":"A Nonlinear Dimensionality Reduction Search Improved Differential Evolution for large-scale optimization","authors":"Yifei Yang , Haotian Li , Zhenyu Lei , Haichuan Yang , Jian Wang","doi":"10.1016/j.swevo.2024.101832","DOIUrl":"10.1016/j.swevo.2024.101832","url":null,"abstract":"<div><div>Large-scale optimization problems present significant challenges due to the high dimensionality of the search spaces and the extensive computational resources required. This paper introduces a novel algorithm, Nonlinear Dimensionality Reduction Enhanced Differential Evolution (NDRDE), designed to address these challenges by integrating nonlinear dimensionality reduction techniques with differential evolution. The core innovation of NDRDE is its stochastic dimensionality reduction strategy, which enhances population diversity and improves the algorithm’s exploratory capabilities. NDRDE also employs a spherical search method to maximize the obliteration of directional information, thus increasing randomness and improving the exploration phase. The algorithm dynamically adjusts the dimensionality of the search space, leveraging a combination of high-dimensional precision search and low-dimensional exploratory search. This approach not only reduces the computational burden but also maintains a high level of accuracy in finding optimal solutions. Extensive experiments on the IEEE CEC large-scale global optimization benchmark problems, including CEC2010 and CEC2013, demonstrate that NDRDE significantly outperforms several state-of-the-art algorithms, showcasing its superiority in tackling large-scale optimization problems. The code for NDRDE will be made publicly available at <span><span>https://github.com/louiseklocky</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101832"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183613","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

Cost optimization, reliability, and MTTF analysis for failed excavators in hydraulic repair center using queueing theory

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101824

Khushbu S. Antala, Sudeep Singh Sanga

{"title":"Cost optimization, reliability, and MTTF analysis for failed excavators in hydraulic repair center using queueing theory","authors":"Khushbu S. Antala, Sudeep Singh Sanga","doi":"10.1016/j.swevo.2024.101824","DOIUrl":"10.1016/j.swevo.2024.101824","url":null,"abstract":"<div><div>In the present study, we utilize the application of queues at construction sites where excavators are used extensively. Excavators are prone to failures requiring timely repairs and maintenance. We establish a hydraulic repair center (HRC) to repair and maintain these failed excavators. The HRC is equipped with a dedicated hydraulic hose crimper (HHC) machine, which acts as the server providing repairs to the arriving failed excavators, referred to as customers. Two types of excavators are considered: crawler excavator (CE) and mini excavator (ME), with ME being given priority in repair jobs over CE. To address realistic situations, various queueing characteristics are incorporated, including a non-preemptive priority rule, a retrial orbit, etc. First, we develop a mathematical model by considering the arrival of excavators at the HRC following a Poisson process, with repair times adhering to exponential distributions. We construct the Markov model by formulating time-dependent differential-difference equations for each system state. These equations are then solved using a matrix method based on spectral theory to develop the corresponding probability distributions. Second, we establish several expressions of queueing and reliability indices. Third, a nonlinear cost function is formulated, and optimized using particle swarm optimization (PSO) algorithm and the bat algorithm (BA).</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101824"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An evolutionary algorithm driving by dimensionality reduction operator and knowledge model for the electric vehicle routing problem with flexible charging strategy

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-01 DOI: 10.1016/j.swevo.2024.101814

Bihao Yang, Teng Ren, Huijuan Yu, Jie Chen, Yaya Wang

{"title":"An evolutionary algorithm driving by dimensionality reduction operator and knowledge model for the electric vehicle routing problem with flexible charging strategy","authors":"Bihao Yang, Teng Ren, Huijuan Yu, Jie Chen, Yaya Wang","doi":"10.1016/j.swevo.2024.101814","DOIUrl":"10.1016/j.swevo.2024.101814","url":null,"abstract":"<div><div>The digital economy and digital technology are promoting the integrated development of industry and digital, forming a new path for industrial upgrading and building a new development pattern.In today's context of digital economy and green transformation, it is a challenging optimization problem to scientifically plan the logistics routes of electric vehicles (EVs) when taking charging strategies into consideration. Aiming at the drawback of supposing a fixed charging rate in the traditional EV routing problems (EVRPs), the charging data of a type of mainstream EVs were collected and the instantaneous charging power was simulated in the real scenario. To solve problems of the fixed charge timing and charged energy in traditional EVRP models and partial charging strategies, a new EVRP model considering the flexible charging strategy (EVRP-FCS) by taking the charged energy as one of the decision variables. To effectively solve the model and fully search in the solution space, an improved evolutionary algorithm was proposed. The performance advantages of the algorithm are determined by comparison of 22 groups of large-scale experimental examples. The experimental results have demonstrated the performance advantages of the algorithm.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101814"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183615","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