Swarm and Evolutionary Computation最新文献_第9页

HAPI-DE: Differential evolution with hierarchical archive based mutation strategy and promising information HAPI-DE：基于分级档案突变策略和有望信息的差异进化论

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-16 DOI: 10.1016/j.swevo.2024.101705

Quanbin Zhang, Zhenyu Meng

{"title":"HAPI-DE: Differential evolution with hierarchical archive based mutation strategy and promising information","authors":"Quanbin Zhang, Zhenyu Meng","doi":"10.1016/j.swevo.2024.101705","DOIUrl":"10.1016/j.swevo.2024.101705","url":null,"abstract":"<div><p>Differential Evolution (DE), as a population-based meta-heuristic global optimization technique, has shown excellent performance in handling optimization problems in continuous spaces. Despite its effectiveness, the DE algorithm suffers from shortcomings such as complexity of parameter selection and limitations of the mutation strategy. Therefore, this paper presents a new strategy for generating trial vectors based on a hierarchical archive, which integrates promising information during evolution with current populations to obtain a good perception of the objective landscape. Moreover, to mitigate mis-scaling by scale factor, an adaptive parameter generation mechanism with hierarchical selection (APSH) is proposed. Furthermore, a novel population diversity metric technique and a restart mechanism based on wavelet functions is introduced in this paper. Comparative experiments were conducted to evaluate the performance of the proposed algorithm using 100 benchmark functions from the CEC2013, CEC2014, CEC2017, and CEC2022 test suites. The results demonstrate that the HAPI-DE algorithm outperforms or is on par with 6 recent powerful DE variants. Additionally, HAPI-DE was utilized in parameter extraction for the photovoltaic model STP6-120/36. The findings suggest that our algorithm, HAPI-DE, demonstrates competitiveness when compared to the 6 other DE variants.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101705"},"PeriodicalIF":8.2,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993317","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 clustering-assisted adaptive evolutionary algorithm based on decomposition for multimodal multiobjective optimization 基于分解的多模式多目标优化聚类辅助自适应进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-13 DOI: 10.1016/j.swevo.2024.101691

Tenghui Hu , Xianpeng Wang , Lixin Tang , Qingfu Zhang

{"title":"A clustering-assisted adaptive evolutionary algorithm based on decomposition for multimodal multiobjective optimization","authors":"Tenghui Hu , Xianpeng Wang , Lixin Tang , Qingfu Zhang","doi":"10.1016/j.swevo.2024.101691","DOIUrl":"10.1016/j.swevo.2024.101691","url":null,"abstract":"<div><p>A multimodal multiobjective optimization problem can have multiple equivalent Pareto Sets (PSs). Since the number of PSs may vary in different problems, if the population is restricted to a fixed size, the number of solutions found for each PS will inevitably fluctuate widely, which is undesirable for decision makers. To address the issue, this paper proposes a clustering-assisted adaptive evolutionary algorithm based on decomposition (CA-MMEA/D), whose search process can be roughly divided into two stages. In the first stage, an initial exploration of decision space is carried out, and then solutions with good convergence are used for clustering to estimate the number and location of multiple PSs. In the second stage, new search strategies are developed on the basis of clustering, which can take advantage of unimodal search methods. Experimental studies show that the proposed algorithm outperforms some state-of-the-art algorithms, and CA-MMEA/D can keep the number of solutions found for each PS at a relatively stable level for different problems, thus making it easier for decision makers to choose the desired solutions. The research in this paper provides new ideas for the design of decomposition-based multimodal multiobjective algorithms.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101691"},"PeriodicalIF":8.2,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141978248","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 improved variable neighborhood search algorithm embedded temporal and spatial synchronization for vehicle and drone cooperative routing problem with pre-reconnaissance 针对具有预侦察功能的车辆和无人机合作路由问题的嵌入时空同步的改进型可变邻域搜索算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-13 DOI: 10.1016/j.swevo.2024.101699

Weichang Sun , Zhihao Luo , Xingchen Hu , Witold Pedrycz , Jianmai Shi

{"title":"An improved variable neighborhood search algorithm embedded temporal and spatial synchronization for vehicle and drone cooperative routing problem with pre-reconnaissance","authors":"Weichang Sun , Zhihao Luo , Xingchen Hu , Witold Pedrycz , Jianmai Shi","doi":"10.1016/j.swevo.2024.101699","DOIUrl":"10.1016/j.swevo.2024.101699","url":null,"abstract":"<div><p>Drones are increasingly utilized for transportation reconnaissance due to their expansive field of view, cost-effectiveness, and agility. They can pre-reconnaissance the traveling routes of the ground vehicles carrying valuable goods to ensure the safety of vehicles and their goods. This rises a novel routing problem for the Vehicles and their Pre-Reconnaissance Drones, which is an integrated point and arc routing problem with temporal and spatial synchronization. A mixed integer linear programming model is developed to formulate the problem with complex synchronization constraints. The Variable Neighborhood Search algorithm integrated with the Temporal & Spatial synchronization-based Greedy search and simulated annealing strategy is designed to solve the model. A practical case based on real urban data from Beijing, China, and random instances with different sizes are tested and compared with the proposed algorithm. Computational results indicated that the proposed algorithm can solve the problem efficiently and outperform the simulated annealing algorithm and the greedy algorithm.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101699"},"PeriodicalIF":8.2,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141978247","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 memetic algorithm for a cooperative task allocation problem of multiple unmanned aerial vehicles in smart agriculture 智能农业中多个无人飞行器合作任务分配问题的基于学习的记忆算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-11 DOI: 10.1016/j.swevo.2024.101694

Teng-Yu Chen , Zhong-Hua Miao , Wei-Min Li , Quan-Ke Pan

{"title":"A learning-based memetic algorithm for a cooperative task allocation problem of multiple unmanned aerial vehicles in smart agriculture","authors":"Teng-Yu Chen , Zhong-Hua Miao , Wei-Min Li , Quan-Ke Pan","doi":"10.1016/j.swevo.2024.101694","DOIUrl":"10.1016/j.swevo.2024.101694","url":null,"abstract":"<div><p>Smart agriculture aligns with the principles of sustainable development, making it a crucial direction for the future agriculture. This study focuses on a cooperative plant protection task allocation problem (CPPTAP) of multiple unmanned aerial vehicles (UAVs) with a common deadline in smart agriculture. CPPTAP permits multiple UAVs to conduct pesticide spraying on the same field. The completion time for each task fluctuates due to the cooperation among UAVs. We present a mathematical model and learning-based memetic algorithm (L-MA) to maximize the total area of the fields to be sprayed. In the evolutionary stage, mutation and repair operators based on value information are applied to balance the exploration and exploitation, while a problem-specific local search strategy is designed to enhance exploitation capability. A knowledge-based UAV allocation method (KUAM) is employed to maximize UAV utilization efficiency and minimize conflicts. Throughout the search process, Q-learning is utilized to assist the aforementioned operators and make decisions on the number of cooperative UAVs on fields. The effectiveness of L-MA is validated by comparing it against other state-of-the-art algorithms. The results demonstrate that L-MA outperforms the compared algorithms at a considerable margin in a statistical sense.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101694"},"PeriodicalIF":8.2,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992979","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 novel evolutionary strategy optimization algorithm for reliability redundancy allocation problem with heterogeneous components 针对异构组件可靠性冗余分配问题的新型进化策略优化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-10 DOI: 10.1016/j.swevo.2024.101695

A.D. Hesampour , K. Ziarati , S. Zarezadeh

{"title":"A novel evolutionary strategy optimization algorithm for reliability redundancy allocation problem with heterogeneous components","authors":"A.D. Hesampour , K. Ziarati , S. Zarezadeh","doi":"10.1016/j.swevo.2024.101695","DOIUrl":"10.1016/j.swevo.2024.101695","url":null,"abstract":"<div><p>The reliability-redundancy allocation problem (RRAP) is an optimization problem that maximizes system reliability under some constraints. In most studies on the RRAP, either active redundant components or cold standby components are used in a subsystem. This paper presents a new model for the RRAP of a system with a mixed redundancy strategy, in which all components can be heterogeneous. This formulation leads to a more precise solution for the problem; however, RRAP is an np-hard problem, and the new mixed heterogeneous model will be more complicated to solve. After formulating the issue, a novel design of an evolutionary strategy optimization algorithm is proposed to solve that. The problem consists of discrete and continuous variables, and different mutation strategies are designed for each. The new formulation of the problem and the new method for solving it lead to better results than those reported in other recent papers. We implement the new suggested heterogeneous model with the PSO and SPSO algorithms to better compare the proposed algorithm. Results show improvement in both system reliability and fitness evaluation count.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101695"},"PeriodicalIF":8.2,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935234","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

Reinforced steering Evolutionary Markov Chain for high-dimensional feature selection 用于高维特征选择的强化转向进化马尔可夫链

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-10 DOI: 10.1016/j.swevo.2024.101701

Atiq ur Rehman, Samir Brahim Belhaouari, Amine Bermak

{"title":"Reinforced steering Evolutionary Markov Chain for high-dimensional feature selection","authors":"Atiq ur Rehman, Samir Brahim Belhaouari, Amine Bermak","doi":"10.1016/j.swevo.2024.101701","DOIUrl":"10.1016/j.swevo.2024.101701","url":null,"abstract":"<div><p>The increasing accessibility of extensive datasets has amplified the importance of extracting insights from high-dimensional data. However, the task of selecting relevant features in these high-dimensional spaces is made more difficult due to the curse of dimensionality. Although Evolutionary Algorithms (EAs) have shown promise in the literature for feature selection, creating EAs for high dimensions is still challenging. To address the problem of feature selection in high dimensions, a novel concept of Evolutionary Reinforced Markov Chain is proposed in this paper. The proposed work has the following contributions and merits: (i) The paradigms of evolutionary computation, reinforcement learning, and Markov chain are incorporated into an integrational framework for feature selection in high dimensional spaces in a recursive manner. (ii) To support the global convergence of the algorithm and manage its computational complexity, a restricted group of the most effective agents is maintained within the evolutionary population. (iii) The dynamic Markov chain process efficiently manages agent evolution and communication, ensuring effective navigation through the search space. (iv) Agents moving in the right way are rewarded with an increase in their associated transition probability, while the agents going in the wrong direction are discouraged with a decrease in their associated transition probabilities; this promotes the establishment of an equilibrium state and leads to convergence. (v) The effective size of successful agents is reduced recursively while progressing through different states to further facilitate the speed of convergence and decrease the number of features. (vi) The performance comparison with state-of-the-art feature selection methods shows a significant improvement and promise of the proposed method over the existing methods.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101701"},"PeriodicalIF":8.2,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141984832","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-multi-task-assisted evolutionary algorithm for constrained multi-objective optimization 约束多目标优化的动态多任务辅助进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-10 DOI: 10.1016/j.swevo.2024.101683

Qianlin Ye , Wanliang Wang , Guoqing Li , Zheng Wang

{"title":"Dynamic-multi-task-assisted evolutionary algorithm for constrained multi-objective optimization","authors":"Qianlin Ye , Wanliang Wang , Guoqing Li , Zheng Wang","doi":"10.1016/j.swevo.2024.101683","DOIUrl":"10.1016/j.swevo.2024.101683","url":null,"abstract":"<div><p>Compared with common multi-objective optimization problems, constrained multi-objective optimization problems demand additional consideration of the treatment of constraints. Recently, many constrained multi-objective evolutionary algorithms have been presented to reconcile the relationship between constraint satisfaction and objective optimization. Notably, evolutionary multi-task mechanisms have also been exploited in solving constrained multi-objective problems frequently with remarkable outcomes. However, previous methods are not fully applicable to solving problems possessing all types of constraint landscapes and are only superior for a certain type of problem. Thus, in this paper, a novel dynamic-multi-task-assisted constrained multi-objective optimization algorithm, termed DTCMO, is proposed, and three dynamic tasks are involved. The main task approaches the constrained Pareto front by adding new constraints dynamically. Two auxiliary tasks are devoted to exploring the unconstrained Pareto front and the constrained Pareto front with dynamically changing constraint boundaries, respectively. In addition, the first auxiliary task stops the evolution automatically after reaching the unconstrained Pareto front, avoiding the waste of subsequent computational resources. A series of experiments are conducted with eight mainstream algorithms on five benchmark problems, and the results confirm the generality and superiority of DTCMO.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101683"},"PeriodicalIF":8.2,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935235","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 Q-learning based artificial bee colony algorithm for solving surgery scheduling problems with setup time 基于 Q 学习的人工蜂群算法，用于解决有设置时间的手术排期问题

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-09 DOI: 10.1016/j.swevo.2024.101686

Ruixue Zhang , Hui Yu , Kaizhou Gao , Yaping Fu , Joong Hoon Kim

{"title":"A Q-learning based artificial bee colony algorithm for solving surgery scheduling problems with setup time","authors":"Ruixue Zhang , Hui Yu , Kaizhou Gao , Yaping Fu , Joong Hoon Kim","doi":"10.1016/j.swevo.2024.101686","DOIUrl":"10.1016/j.swevo.2024.101686","url":null,"abstract":"<div><p>With the increasing demand for surgeries, surgery scheduling become an important problem in hospital management. Efficient surgery scheduling can enhance the optimal use of surgical resources, leading to high efficiency of surgery assignments. This work addresses surgery scheduling problems with surgical resources setup time. A mathematical model is established to describe the considered problems with the objective of minimizing the maximum completion time of the surgeries (makespan). Second, a modified artificial bee colony (ABC) algorithm is proposed, named QABC. Six local search operators are developed based on the characteristics of the problem, aiming to strengthen the local search capability of the algorithm. To further improve the performance of the algorithm, this study combines a Q-learning strategy with ABC algorithm. During each iteration of the algorithm, the Q-learning strategy is employed to guide the selection of search operators. Finally, the effectiveness of the local search operators and Q-learning based local search selection is verified by solving 20 cases with varying scales. And the results obtained by the Gurobi solver are compared with the proposed QABC. Furthermore, the proposed QABC is compared with the state-of-the-art algorithms. The experimental results and comparisons show that QABC is more effective than its peers for solving the surgery scheduling problems with setup time.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101686"},"PeriodicalIF":8.2,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935237","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 self-adaptive memetic algorithm with Q-learning for solving the multi-AGVs dispatching problem 用 Q-learning 自适应记忆算法解决多AGV 调度问题

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-09 DOI: 10.1016/j.swevo.2024.101697

Chang-Zhe Zheng , Hong-Yan Sang , Li-Ning Xing , Wen-Qiang Zou , Lei-Lei Meng , Tao Meng

{"title":"A self-adaptive memetic algorithm with Q-learning for solving the multi-AGVs dispatching problem","authors":"Chang-Zhe Zheng , Hong-Yan Sang , Li-Ning Xing , Wen-Qiang Zou , Lei-Lei Meng , Tao Meng","doi":"10.1016/j.swevo.2024.101697","DOIUrl":"10.1016/j.swevo.2024.101697","url":null,"abstract":"<div><p>In this paper, we address the problem of dispatching multiple automated guided vehicles (AGVs) in an actual production workshop, aiming to minimize the transportation cost. To solve this problem, a self-adaptive memetic algorithm with Q-learning (Q-SAMA) is proposed. An improved nearest-neighbor task division heuristic is used for generating premium solutions. Additionally, a Q-learning is integrated to select appropriate neighborhood operators, thereby enhancing the algorithm's exploration ability. To prevent the algorithm from falling into a local optimum, the restart strategy is offered. In order to adapt Q-SAMA to different stages in the search process, the traditional crossover and mutation probabilities are no longer used. Instead, a self-adaptive probability is obtained based on the population's degree of concentration, and the sparsity relationship among individuals' fitness. Finally, experimental results validate the effectiveness of the proposed method. It is able to yield better results compared with other five state-of-the-art algorithms.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101697"},"PeriodicalIF":8.2,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935236","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

Constraint landscape knowledge assisted constrained multiobjective optimization 约束景观知识辅助约束多目标优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-09 DOI: 10.1016/j.swevo.2024.101685

Yuhang Ma , Bo Shen , Anqi Pan , Jiankai Xue

{"title":"Constraint landscape knowledge assisted constrained multiobjective optimization","authors":"Yuhang Ma , Bo Shen , Anqi Pan , Jiankai Xue","doi":"10.1016/j.swevo.2024.101685","DOIUrl":"10.1016/j.swevo.2024.101685","url":null,"abstract":"<div><p>When evolutionary algorithms are employed to tackle constrained multiobjective optimization problems (CMOPs), constraint handling techniques (CHTs) play a pivotal role. To date, several CHTs have been designed, but they are only effective for certain types of constraint landscapes. For CMOPs with unknown properties, their optimization performance and efficiency remain uncertain. To tackle this issue, we attempt to mine and utilize the knowledge of constraint landscape to solve CMOPs. Specifically, the evolutionary process can be divided into three stages: learning stage, classification stage, and evolving stage. During the learning stage, the two populations, namely <em>mainPop</em> and <em>auxPop</em>, cooperatively evolve with and without considering constraints, respectively. The <em>mainPop</em> can locate the feasible regions, while the <em>auxPop</em> is employed to evaluate the size of the feasible regions. Subsequently, in the classification stage, based on the learned landscape knowledge, the category of problem can be determined: CMOP with small feasible regions or CMOP with large feasible regions. Then, in the evolving stage, for CMOPs with small feasible regions, CHTI, which includes a population exchange method and a feasible regions relaxation method, is proposed, while for CMOPs with large feasible regions, CHTII, which encompasses a dynamic resource allocation method and a coevolutionary method, is designed. The proposed framework is executed on extensive benchmark test suites. It has achieved superior or at least competitive performance compared with other state-of-the-art algorithms. Furthermore, the framework has been successfully implemented on the robotic manipulator path planning problem.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101685"},"PeriodicalIF":8.2,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935097","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