[1]张　萌,梁　静,乔康加,等.基于竞争与合作多任务的约束多目标进化算法[J].郑州大学学报(工学版),2026,47(02):51-58(76).[doi:10.13705/j.issn.1671-6833.2025.05.021]
　ZHANG Meng,LIANG Jing,QIAO Kangjia,et al.A Constrained Multi-objective Evolutionary Algorithm Based on Competition and Cooperation Multi-tasking[J].Journal of Zhengzhou University (Engineering Science),2026,47(02):51-58(76).[doi:10.13705/j.issn.1671-6833.2025.05.021]
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基于竞争与合作多任务的约束多目标进化算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年02期
页码:
51-58(76)
栏目:
出版日期:
2026-02-13

文章信息/Info

Title:
A Constrained Multi-objective Evolutionary Algorithm Based on Competition and Cooperation Multi-tasking
文章编号:
1671-6833(2026)02-0051-08
作者:
张　萌1;  梁　静2;  乔康加2;  岳彩通2;  王曦璐3
1.河南牧业经济学院 能源与智能工程学院,河南 郑州 450046;2.郑州大学 电气与信息工程学院,河南 郑州 450001;3.萨里大学 计算机科学与电子工程学院,英国 萨里 GU2 7XH
Author(s):
ZHANG Meng1;  LIANG Jing2;  QIAO Kangjia2;  YUE Caitong2;  WANG Xilu3
1.School of Energy and Intelligent Engineering, Henan College of Animal Husbandry and Economics, Zhengzhou 450046, China;2.School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China;3.School of Computer Science and Electronic Engineering, University of Surrey, Surrey GU2 7XH, U. K.
关键词:
约束多目标优化;  进化算法;  多任务;  资源分配;  协同优化
Keywords:
constrained multi-objective optimization;  evolutionary algorithm;  multi-tasking;  resource allocation;  collaborative optimization
分类号:
TP301
DOI:
10.13705/j.issn.1671-6833.2025.05.021
文献标志码:
A
摘要:
基于多任务的约束多目标进化算法在资源分配和协同优化方面存在不足,导致低有效性种群浪费计算资源以及优质解信息未被充分利用。因此,设计了一种基于竞争与合作多任务的约束多目标进化算法,包含两个主要的策略:第一,提出一种基于竞争的资源分配策略,基于各任务种群的历史表现实现计算资源的自适应分配;第二,设计了基于父代聚合和子代扩散的协同优化策略,通过跨任务合作生成高质量的子代,并使子代扩散到各任务种群中,实现有效信息的高效利用。提出的算法与其他5种先进算法(CMOEA_MS、cDPEA、EMCMO、MTCMO、CMOEMT)在38个测试函数上进行对比实验。结果表明:所提算法在IGD和HV指标上分别在25个和26个函数上取得了最优结果,且分别至少在23个和24个函数上优于对比算法;所提算法在所有函数上的可行率达到100%,能够有效求解约束多目标优化问题。
Abstract:
Constrained multi-objective evolutionary algorithm based on multi-tasking competition and cooperation has problems in resource allocation and collaborative optimization, resulting in low effectiveness populations wasting computational resources, and underutilized high-quality solution information. Therefore, in this study, a constrained multi-objective evolutionary algorithm based on competitive and cooperation multitasking was proposed, which included two main strategies. Firstly, a competition-based resource allocation strategy was proposed to achieve adaptive allocation of computing resources based on the historical performance of each task population. Secondly, a collaborative optimization strategy based on parent aggregation and offspring diffusion was designed to generate highquality offspring through cross-task cooperation and spread them to various task populations, achieving efficient utilization of effective information. The proposed algorithm was compared with five other advanced algorithms (CMOEA_MS, cDPEA, EMCMO, MTCMO, and CMOEMT) on 38 test functions, and the results showed that the proposed algorithm achieved optimal results on 25 and 26 functions with IGD and HV indicators, respectively, and was superior to the compared algorithms on at least 23 and 24 functions, respectively. The proposed algorithm had a feasibility rate of 100% on all functions and can effectively solve constrained multi-objective optimization problems.

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更新日期/Last Update: 2026-03-04