IEEE Transactions on Evolutionary Computation最新文献_第9页

Speeding up Local Search for the Indicator-Based Subset Selection Problem by a Candidate List Strategy 用候选列表策略加速基于指标的子集选择问题的局部搜索

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-02-05 DOI: 10.1109/tevc.2025.3538902

Keisuke Korogi, Ryoji Tanabe

引用次数: 0

How Do Dynamic Events Change the Fitness Landscape of Traveling Salesman Problems? 动态事件如何改变旅行推销员问题的适应度景观？

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-02-04 DOI: 10.1109/tevc.2025.3538547

Hao Tong, Miqing Li, Jialin Liu, Xin Yao

引用次数: 0

MOTEA-II: A Collaborative Multiobjective Transformation-Based Evolutionary Algorithm for Bilevel Optimization MOTEA-II：一种基于协同多目标变换的双层次优化进化算法

IF 11.7 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-02-04 DOI: 10.1109/TEVC.2025.3538611

Lei Chen;Yiu-Ming Cheung;Hai-Lin Liu;Yutao Lai

{"title":"MOTEA-II: A Collaborative Multiobjective Transformation-Based Evolutionary Algorithm for Bilevel Optimization","authors":"Lei Chen;Yiu-Ming Cheung;Hai-Lin Liu;Yutao Lai","doi":"10.1109/TEVC.2025.3538611","DOIUrl":"10.1109/TEVC.2025.3538611","url":null,"abstract":"Evolutionary algorithms (EAs) for optimization have received wide attention due to their robustness and practicality. However, the traditional way of asynchronously handling bilevel optimization problems (BLOPs) ignores the benefits brought by effective upper- and lower-level collaboration. To address this issue, this article proposes a collaborative multiobjective transformation (MOT)-based EA (MOTEA-II). In MOTEA-II, the BLOP is handled within a decomposition-based multiobjective optimization paradigm using a two-stage collaborative MOT strategy. The stage-1 MOT focuses on multiple lower-level optimizations and collaboration, while stage-2 collaborates the upper-level optimization with lower-level optimization, which makes simultaneously horizontal and vertical optimization information sharing in bilevel optimization possible. In addition, a dynamic decomposition strategy is further proposed to reconstruct the hierarchy relationship in collaborative multiobjective optimization, facilitating the adaptive and flexible importance control of the upper-level objective optimization and lower-level optimality satisfaction for better-bilevel search efficiency. Empirical studies are conducted on two groups of commonly used BLOP benchmark suites and four practical applications. Experimental results show that the proposed collaborative MOTEA-II can achieve performance comparable to that of the previous MOTEA and three other representative EA-based bilevel optimization approaches, but using much fewer computational resources.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"29 2","pages":"474-489"},"PeriodicalIF":11.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870353","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125358","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

Dealing With Structure Constraints in Evolutionary Pareto Set Learning 进化Pareto集学习中结构约束的处理

IF 11.7 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-02-03 DOI: 10.1109/TEVC.2025.3537986

Xi Lin;Xiaoyuan Zhang;Zhiyuan Yang;Qingfu Zhang

{"title":"Dealing With Structure Constraints in Evolutionary Pareto Set Learning","authors":"Xi Lin;Xiaoyuan Zhang;Zhiyuan Yang;Qingfu Zhang","doi":"10.1109/TEVC.2025.3537986","DOIUrl":"10.1109/TEVC.2025.3537986","url":null,"abstract":"In the past few decades, many multiobjective evolutionary optimization algorithms (MOEAs) have been proposed to find a finite set of approximate Pareto solutions for a given problem in a single run. However, in many real-world applications, it could be desirable to have structure constraints on the entire optimal solution set, which define the patterns shared among all solutions. The current population-based MOEAs cannot properly handle such requirements. In this work, we make a first attempt to incorporate the structure constraints into the whole solution set. Specifically, we propose to model such a multiobjective optimization problem as a set optimization problem with structure constraints. The structure constraints define some patterns that all the solutions are required to share. Such patterns can be fixed components shared by all solutions, specific relations among decision variables, and the required shape of the Pareto set. In addition, we develop a simple yet efficient evolutionary stochastic optimization method to learn the set model, which only requires a low computational budget similar to classic MOEAs. With our proposed method, the decision-makers can easily tradeoff the Pareto optimality with preferred structures, which is not supported by other MOEAs. A set of experiments on benchmark test suites and real-world application problems demonstrates that our proposed method is effective.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"29 3","pages":"616-630"},"PeriodicalIF":11.7,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083169","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

Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects 通用人工智能系统设计与丰富的进化计算：综述与展望

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-01-30 DOI: 10.1109/tevc.2025.3530096

Daniel Molina, Javier Poyatos, Javier Del Ser, Salvador García, Hisao Ishibuchi, Isaac Triguero, Bing Xue, Xin Yao, Francisco Herrera

引用次数: 0

IEEE Computational Intelligence Society Information IEEE计算智能学会信息

IF 11.7 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-01-30 DOI: 10.1109/TEVC.2025.3529241

引用次数: 0

IEEE Transactions on Evolutionary Computation Publication Information IEEE进化计算汇刊信息

IF 11.7 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-01-30 DOI: 10.1109/TEVC.2025.3527657

引用次数: 0

IEEE Transactions on Evolutionary Computation Information for Authors IEEE演化计算资讯汇刊

IF 11.7 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-01-30 DOI: 10.1109/TEVC.2025.3529239

引用次数: 0

A Deep Reinforcement Learning-Assisted Multimodal Multiobjective Bilevel Optimization Method for Multirobot Task Allocation 一种深度强化学习辅助的多模态多目标双级优化多机器人任务分配方法

IF 11.7 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-01-28 DOI: 10.1109/TEVC.2025.3535954

Yuanyuan Yu;Qirong Tang;Qingchao Jiang;Qinqin Fan

{"title":"A Deep Reinforcement Learning-Assisted Multimodal Multiobjective Bilevel Optimization Method for Multirobot Task Allocation","authors":"Yuanyuan Yu;Qirong Tang;Qingchao Jiang;Qinqin Fan","doi":"10.1109/TEVC.2025.3535954","DOIUrl":"10.1109/TEVC.2025.3535954","url":null,"abstract":"Multirobot task allocation (MRTA) is a challenging bi-level problem in the multirobot cooperative systems (MRCSs) and offers an effective method for addressing complex tasks. However, dynamic /uncertain environments can easily invalidate original schemes in practical MRTA decision-makings. Further, a nested structure in MRTA problems makes computational expensive. Therefore, the two main tasks are 1) finding a sufficient number of equivalent schemes for MRTA problems to adapt to task environments and 2) improving algorithm search efficiency in bi-level optimization problems. In this study, a multimodal multiobjective evolutionary algorithm (MMOEA) based on deep reinforcement learning (DRL) and large neighborhood search (LNS), called MMOEA-DL, is proposed to solve MRTA problems. In the MMOEA-DL, the task allocation problem, which is considered as the upper-level optimization problem, is solved using an improved MMOEA. The traveling salesman problem (TSP) regarded as the lower-level optimization problem is addressed via end-to-end method (i.e., DRL) and LNS. By leveraging the end-to-end method to obtain the results of the lower-level optimization, the bi-level optimization problem is effectively transformed into a single-level optimization problem. To demonstrate the performance of the proposed algorithm, 16 MRTA simulation scenarios and two actual MRTA scenarios with evenly and unevenly distributed task points are introduced in the present study. The simulation results verify that the MMOEA-DL not only provides decision-makers with expanded equivalent optimal schemes to address dynamic environments or unforeseen circumstances, but also offers a novel approach to solve the multimodal multiobjective bi-level optimization problem while saving computational costs.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"29 3","pages":"574-588"},"PeriodicalIF":11.7,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055093","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

Particle-Assisted Deep Reinforcement Learning for Quantum State Manipulation 量子态操纵的粒子辅助深度强化学习

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-01-27 DOI: 10.1109/tevc.2025.3534530

Haixu Yu, Xiang Liu, Bohui Wang, Xudong Zhao

引用次数: 0