Swarm and Evolutionary Computation最新文献_第4页

Energy-efficient task scheduling with binary random faults in cloud computing environments

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-26 DOI: 10.1016/j.swevo.2025.101877

Lei Jin , Jie Yuan , Dequn Zhou , Xiuzhi Sang , Shi Chen , Xianyu Yu , Guohui Lin

{"title":"Energy-efficient task scheduling with binary random faults in cloud computing environments","authors":"Lei Jin , Jie Yuan , Dequn Zhou , Xiuzhi Sang , Shi Chen , Xianyu Yu , Guohui Lin","doi":"10.1016/j.swevo.2025.101877","DOIUrl":"10.1016/j.swevo.2025.101877","url":null,"abstract":"<div><div>Fault management and energy consumption control have become focal topics in the rapid development of cloud computing services. This paper addresses the task scheduling problem with binary random faults in the networking and power supply of cloud computing environments and proposes a task scheduling model with the multiobjectives of minimizing energy consumption and task completion time while maximizing task completion rate. An estimation of distribution algorithm (EDA) with crowding distance (C) and neighborhood search (N) (EDA-CN) is designed for the model, into which a multi-model probability matrix, regional dislocation backup mechanism, neighborhood search operator, and crowding distance operator are integrated. Numerical experiments examine the effectiveness of EDA-CN in comparison with EDA, EDA-C, and the classic non-dominated sorting genetic algorithm III (NSGA3). The results show that EDA-CN consistently outperformed EDA and EDAC, and EDA-CN and NSGA3 performed comparably often yet EDA-CN still outperformed statistically significantly.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101877"},"PeriodicalIF":8.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487028","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

Online feature subset selection for mining feature streams in big data via incremental learning and evolutionary computation

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-26 DOI: 10.1016/j.swevo.2025.101896

Yelleti Vivek , Vadlamani Ravi , P. Radha Krishna

{"title":"Online feature subset selection for mining feature streams in big data via incremental learning and evolutionary computation","authors":"Yelleti Vivek , Vadlamani Ravi , P. Radha Krishna","doi":"10.1016/j.swevo.2025.101896","DOIUrl":"10.1016/j.swevo.2025.101896","url":null,"abstract":"<div><div>Online streaming feature subset selection (OSFSS) presents a noteworthy challenge when data samples arrive rapidly and in a time-dependent manner. The complexity of this problem is further exacerbated when features arrive as a stream. Despite several attempts to solve OSFSS over feature streams, existing methods lack scalability, cannot handle interaction effects among features, and fail to efficiently handle high-velocity feature streams. To address these challenges, we propose a novel wrapper-for OSFSS named OSFSS-W (wrapper-for OSFSS), specifically designed to mine feature streams within the Apache Spark environment. Our proposed method incorporates (i) two vigilance tests: for removing (a) irrelevant features and (b) redundant features (ii) incremental learning and (iii) a tolerance-based feedback mechanism that retains and utilizes previous knowledge while adhering to the predefined tolerance thresholds. Additionally, for the purpose of optimization, we introduce a Bare Bones Particle Swarm Optimization (BBPSO-L) algorithm driven by the logistic distribution. Further, the BBPSO-L is parallelized within Apache Spark, following an island-based approach. We evaluated the performance of the proposed algorithm on the datasets taken from the cybersecurity, bioinformatics, and finance domains. The results demonstrate that incorporating two vigilance tests coupled with a tolerance-based feedback mechanism significantly improved the median Area under the receiver operating characteristic curve (AUC) and median cardinality across all datasets.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101896"},"PeriodicalIF":8.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487580","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 learning-based dual-population optimization algorithm for hybrid seru system scheduling with assembly

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-25 DOI: 10.1016/j.swevo.2025.101901

Yuting Wu , Ling Wang , Rui Li , Yuxiang Xu , Jie Zheng

{"title":"A learning-based dual-population optimization algorithm for hybrid seru system scheduling with assembly","authors":"Yuting Wu , Ling Wang , Rui Li , Yuxiang Xu , Jie Zheng","doi":"10.1016/j.swevo.2025.101901","DOIUrl":"10.1016/j.swevo.2025.101901","url":null,"abstract":"<div><div>As the personalized demand increases, the hybrid seru system (HSS) has emerged as an efficient production paradigm to address the volatile market and intricate production conditions due to its reconfigurability. To satisfy the actual production demands, it is common to consider multiple assembly stages in the HSS. However, the increasing complexity poses challenges for the design of scheduling optimization algorithms. In this paper, a learning-based dual-population optimization algorithm (LDPOA) is designed for the hybrid seru system scheduling problem with assembly. Based on a problem-specific decomposition paradigm, a dual-population cooperative search framework is proposed to enhance the exploration capability by focusing on different subproblem optimizations in different populations. During the evolution, a fusion strategy and filtering mechanism are designed to avoid invalid searches by allocating computing resources to more potential individuals. A learning-guided search mode selection strategy and a population communication strategy are proposed to further improve search efficiency and population diversity. Finally, the adjustment strategies are proposed to improve the solution quality by leveraging problem knowledge. Extensive experiments are conducted to assess the performance of the LDPOA. The comparisons show that the HSS can improve production efficiency by 35.3 % compared to the traditional manufacturing mode.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101901"},"PeriodicalIF":8.2,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-24 DOI: 10.1016/j.swevo.2025.101890

Liangying Wang , Lihuan Hong , Haoxuan Fu , Zhiling Cai , Yiwen Zhong , Lijin Wang

{"title":"Adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update","authors":"Liangying Wang , Lihuan Hong , Haoxuan Fu , Zhiling Cai , Yiwen Zhong , Lijin Wang","doi":"10.1016/j.swevo.2025.101890","DOIUrl":"10.1016/j.swevo.2025.101890","url":null,"abstract":"<div><div>In recent years, multi-objective particle swarm optimization algorithms have been widely used in science and engineering due to their advantages of fast convergence speed and easy implementation. However, the selection of globally optimal particle is an important and challenging problem in the design of multi-objective particle swarm optimization algorithms. In this regard, this paper proposes an adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update, named ADMOPSO. First, an adaptive penalty-based boundary intersection (PBI) distance strategy is designed to select the globally optimal particle from two elite particles which are randomly chosen from an elite particle set. This strategy better balances the diversity and convergence requirements of particle swarm optimization algorithm in the optimization process. Second, a simple position probabilistic update strategy is constructed to rewrite the velocity update method with the weight and use the learning rate to control the scale of the updated velocity in the position update equation to avoid particle swarm falling into the local optimum. Finally, an extensive experimental study is conducted to test the performance of several selected multi-objective optimization algorithms on ZDT, WFG and DTLZ benchmark problems, as well as 7 real-world problems were conducted to test the proposed algorithm. Comparative experimental results show that the algorithm proposed in this paper has significant advantages over other algorithms. This shows that the ADMOPSO algorithm is competitive in dealing with multi-objective problems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101890"},"PeriodicalIF":8.2,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474774","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

Learning-assisted improvements in Adaptive Variable Neighborhood Search

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-24 DOI: 10.1016/j.swevo.2025.101887

Panagiotis Karakostas, Angelo Sifaleras

{"title":"Learning-assisted improvements in Adaptive Variable Neighborhood Search","authors":"Panagiotis Karakostas, Angelo Sifaleras","doi":"10.1016/j.swevo.2025.101887","DOIUrl":"10.1016/j.swevo.2025.101887","url":null,"abstract":"<div><div>This study presents the design and integration of novel adaptive components within the Double-Adaptive General Variable Neighborhood Search (DA-GVNS) algorithm, aimed at improving its overall efficiency. These adaptations utilize iteration-based data to refine the search process, with enhancements such as an adaptive reordering mechanism in the refinement phase and a knowledge-guided approach to adjust the search strategy. Additionally, an adaptive mechanism for dynamically controlling the shaking intensity was introduced. The proposed knowledge-guided adaptations demonstrated superior performance over the original DA-GVNS framework, with the most effective scheme selected for further evaluation. Initially, the symmetric Traveling Salesman Problem (TSP) was used as a benchmark to quantify the impact of these mechanisms, showing significant improvements through rigorous statistical analysis. A comparative study was then conducted against six advanced heuristics from the literature. Finally, the most promising knowledge-guided GVNS (KG-GVNS) was tested against the original DA-GVNS on selected instances of the Quadratic Assignment Problem (QAP), where detailed statistical analysis highlighted its competitive advantage and robustness in addressing complex combinatorial optimization problems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101887"},"PeriodicalIF":8.2,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143478655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reinforcement learning assisted differential evolution with adaptive resource allocation strategy for multimodal optimization problems

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-21 DOI: 10.1016/j.swevo.2025.101888

Tao Ma , Hong Zhao , Xiangqian Li , Fang Yang , Chun-sheng Liu , Jing Liu

{"title":"Reinforcement learning assisted differential evolution with adaptive resource allocation strategy for multimodal optimization problems","authors":"Tao Ma , Hong Zhao , Xiangqian Li , Fang Yang , Chun-sheng Liu , Jing Liu","doi":"10.1016/j.swevo.2025.101888","DOIUrl":"10.1016/j.swevo.2025.101888","url":null,"abstract":"<div><div>Multimodal optimization problems (MMOPs) present the challenge of identifying multiple optimal solutions within a search space, requiring algorithms to effectively balance exploration and exploitation. To enhance solution accuracy, the local search methods often focus on elite individuals, allocating additional fitness evaluations (FEs) to refine their solutions. However, once the optima near these elite individuals are identified, continued allocation of FEs becomes inefficient, leading to a waste of limited resources. This highlights the inherent difficulty of achieving a balance between exploration and exploitation within the population under constrained resources. To solve this problem, this paper proposes a new reinforcement learning-assisted differential evolution (RLDE) algorithm with adaptive resource allocation strategy. Firstly, the exploitation population is proposed, and the original population focuses on exploring undiscovered optimal regions and generating exploitation populations, while each exploitation population focuses on finding high-precision optima within its responsible optimal region. Secondly, a reinforcement learning-assisted adaptive resource allocation (RLRA) strategy is proposed to allocate FEs, which can reduce the waste of FEs and balance the exploration and exploitation ability among multiple populations. Finally, a local greedy mutation (LGM) strategy is proposed to help individuals evolve toward the neighborhood with better fitness values. Compared with 11 state-of-the-art multimodal algorithms, the RLDE achieves better or more competitive results in all accuracy levels. Besides, the results on the dielectric composite optimization problem verify the practicality of RLDE.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101888"},"PeriodicalIF":8.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453206","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 indicator-based multi-objective evolutionary algorithm assisted by improved graph convolutional networks

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-21 DOI: 10.1016/j.swevo.2025.101892

Pengguo Yan , Ye Tian , Yu Liu

{"title":"An indicator-based multi-objective evolutionary algorithm assisted by improved graph convolutional networks","authors":"Pengguo Yan , Ye Tian , Yu Liu","doi":"10.1016/j.swevo.2025.101892","DOIUrl":"10.1016/j.swevo.2025.101892","url":null,"abstract":"<div><div>Recently, graph convolutional networks (GCN) have attracted significant attention due to their superior performance in handling non-Euclidean spaces, which enables GCN to model and analyze complex data structures that cannot be handled by traditional methods. Neural network-based multi-objective evolutionary algorithms (NNMOEAs) have made significant strides, predominantly focusing on mapping the decision space to the objective space, but may fail to focus on the interconnectedness of solutions within the decision space. To address this problem, this paper proposes a two-stage multi-objective optimization algorithm that utilizes graph convolutional networks to enhance population evolution. In the initial stage, the algorithm employs cosine similarity to represent the population as graph-structured data. A hypervolume-guided self-attention update mechanism is then introduced to balance exploration and exploitation, achieved by establishing an exploratory neighborhood population alongside an expanded neighborhood population. In the subsequent stage, a key node detection strategy is implemented, which considers both the global influence and local mediation roles of nodes. This strategy selects individuals with highly concentrated information to generate new solutions, thereby facilitating a thorough exploration of the solution space. The proposed algorithm is evaluated against five state-of-the-art MOEAs across five benchmark test suites and five real-world problems. The experimental results demonstrate its superior performance in addressing robust, variable linkages and imbalance mapping multi-objective optimization problems, as well as its feasibility in practical problems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101892"},"PeriodicalIF":8.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464854","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 discrete water source cycle algorithm design for solving production scheduling problem in flexible manufacturing systems

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-21 DOI: 10.1016/j.swevo.2025.101897

Wenxiang Xu , Shimin Xu , Junyong Liang , Tao Qin , Dezheng Liu , Lei Wang

{"title":"A discrete water source cycle algorithm design for solving production scheduling problem in flexible manufacturing systems","authors":"Wenxiang Xu , Shimin Xu , Junyong Liang , Tao Qin , Dezheng Liu , Lei Wang","doi":"10.1016/j.swevo.2025.101897","DOIUrl":"10.1016/j.swevo.2025.101897","url":null,"abstract":"<div><div>Aiming at solving the production scheduling problems in flexible manufacturing systems including the flexible job shop scheduling (FJSP) and distributed flexible job shop scheduling (DFJSP) with operation outsourcing, which are two kinds of typical NP-hard problems, the general mathematical model with two optimization objectives including minimizing the total costs as well as makespan are developed. Then, an innovative discrete water source cycle algorithm (IDWCA) inspired by the water cycle process is proposed to address the model. In the IDWCA, the operators including evaporation mixing, precipitation, local mixing, modification of water source composition and water source loss are designed to search for optimization solutions. Finally, 15 FJSP comparison experiments and 45 DFJSP comparison experiments with different scales are provided to verify the comprehensive performance of the IDWCA, in which the IDWCA, the original water cycle algorithm (OWCA), and the two general meta-heuristic algorithms genetic algorithm (GA) and particle swarm optimization (PSO) are involved. Compared with OWCA, GA and PSO, IDWCA performs significantly better in all FJSP experiments, while it performs better in 43 out of 45 DFJSP experiments, and its advantages are more significant in solving the medium-scale and large-scale problems. In addition, the evolutionary curves of the above algorithms indicate that the IDWCA has the better convergence speed and results than that of OWCA, GA and PSO. Therefore, the developed mathematical model and IDWCA are effective in solving the studied FJSP and DFJSP, the proposed algorithm enriches the theoretical researches on meta-heuristic algorithms and production scheduling.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101897"},"PeriodicalIF":8.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep reinforcement learning-based memetic algorithm for solving dynamic distributed green flexible job shop scheduling problem with finite transportation resources

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-21 DOI: 10.1016/j.swevo.2025.101885

Xinxin Zhou , Fuyu Wang , Bin Wu , Yan Li , Nannan Shen

{"title":"Deep reinforcement learning-based memetic algorithm for solving dynamic distributed green flexible job shop scheduling problem with finite transportation resources","authors":"Xinxin Zhou , Fuyu Wang , Bin Wu , Yan Li , Nannan Shen","doi":"10.1016/j.swevo.2025.101885","DOIUrl":"10.1016/j.swevo.2025.101885","url":null,"abstract":"<div><div>To solve the dynamic distributed green flexible job shop scheduling problem with integrated multi-automated guided vehicles (AGVs) transportation (DDGFJSP-MT), a coupled mathematical model is constructed in this study with the objective of minimizing the makespan and total carbon emissions. The complex coupled roles between factories, jobs, machines, and AGVs induced by machine breakdown are explored. Meanwhile, a deep Q-network-based dynamic efficient memetic algorithm (DQN-DEMA) is proposed to solve the problem. First, a four-layer coding is designed to characterize the DDGFJSP-MT, and a novel dynamic decoding technique is developed based on the state variations of the involved subjects and their strong coupling effects following the machine breakdown. Second, an alternating hybrid initialization strategy is employed to improve the quality and diversity of the rescheduled population. Then, several neighborhood search structures are designed based on critical path and bottleneck operation, and DQN is applied to recommend the most suitable local search operator for each elite individual, accelerating the convergence of the rescheduled population and effectively avoiding the waste of algorithmic resources. Finally, performance validation on 20 instances demonstrates that DQN-DEMA obtains the Pareto frontier with higher quality and diversity in 15 instances compared to the six state-of-the-art algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101885"},"PeriodicalIF":8.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Agent Reinforcement Learning for task allocation in the Internet of Vehicles: Exploring benefits and paving the future

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-02-20 DOI: 10.1016/j.swevo.2025.101878

Inam Ullah , Sushil Kumar Singh , Deepak Adhikari , Habib Khan , Weiwei Jiang , Xiaoshan Bai

{"title":"Multi-Agent Reinforcement Learning for task allocation in the Internet of Vehicles: Exploring benefits and paving the future","authors":"Inam Ullah , Sushil Kumar Singh , Deepak Adhikari , Habib Khan , Weiwei Jiang , Xiaoshan Bai","doi":"10.1016/j.swevo.2025.101878","DOIUrl":"10.1016/j.swevo.2025.101878","url":null,"abstract":"<div><div>The Internet of Vehicles (IoV) and its applications are undergoing massive development, requiring diverse autonomous or self-directed vehicles/agents to fulfill various objective and responsibilities in vehicular technology. Similarly, Multi-Agent Systems (MAS) and multi-agent task allocation are currently the main focus of multiple researchers and scholars, and they play a key role in IoV and the Internet of Things (IoT). The development of the IoV and autonomous vehicles plays a significant role in Intelligent Transportation Systems (ITS), which are empowered by vehicular networks. However, the dynamic nature of these networks presents substantial challenges that need to be addressed. In this regard, we trace the historical evolution of the multi-agent task allocation of IoV, highlight its fundamentals and progress, and discuss the existing survey works. This paper comprehensively reviews various IoV strategies, both multi-agent task allocation strategies and Multi-Agent Reinforcement Learning (MARL), emphasizing the intelligent learning architecture, concepts, and security-related issues. Additionally, we highlight various computing platforms and the diverse applications of multi-agent task allocation in IoV, where task allocation is challenging and presents security concerns of multi-agent task allocation in IoV. Finally, we discuss major open problems regarding multi-agent task allocation scalability, complexity, communication overhead, resource allocation, security, privacy, etc., and potential future perspectives on multi-agent task allocation methods are highlighted.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101878"},"PeriodicalIF":8.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453205","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