IEEE Transactions on Evolutionary Computation最新文献_第8页

Expensive Optimization via Relation 基于关系的昂贵优化

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-02-14 DOI: 10.1109/tevc.2025.3542303

Hao Hao, Xiaoqun Zhang, Aimin Zhou

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MetaDE: Evolving Differential Evolution by Differential Evolution 元：通过差异进化来进化差异进化

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-02-13 DOI: 10.1109/tevc.2025.3541587

Minyang Chen, Chenchen Feng, Ran Cheng

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Many-Problem Surrogates for Transfer Evolutionary Multiobjective Optimization With Sparse Transfer Stacking 稀疏迁移叠加迁移进化多目标优化的多问题替代算法

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-02-13 DOI: 10.1109/tevc.2025.3541971

Hao Li, Fanggao Wan, Maoguo Gong, A. K. Qin, Yue Wu, Lining Xing

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Dynamic Cascaded Flow-Shop Scheduling Using an Evolutionary Greedy Algorithm 基于进化贪婪算法的动态级联流水车间调度

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-02-13 DOI: 10.1109/tevc.2025.3541959

Qiu-Ying Li, Quan-Ke Pan, Ling Wang, Liang Gao, Wei-Min Li

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Data-Driven Evolutionary Algorithm Based on Inductive Graph Neural Networks for Multimodal Multi-Objective Optimization 基于归纳图神经网络的多模态多目标优化数据驱动进化算法

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-02-11 DOI: 10.1109/tevc.2025.3541046

Qianlong Dang, Qiqi Liu, Shuai Yang, Xiaoyu He

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Imitation Learning-Assisted Evolutionary Algorithm for Energy-Efficient Flexible Job Shop Scheduling Problem With Automated Guided Vehicles 具有自动导引车的节能柔性作业车间调度问题的模仿学习辅助进化算法

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-02-10 DOI: 10.1109/tevc.2025.3540105

Weiyao Cheng, Leilei Meng, Biao Zhang, Kaizhou Gao, Hongyan Sang

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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

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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