[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(XX):1-9.[doi:10.13705/j.issn.1671-6833.2025.05.021]
　ZHANG Meng,JING Liang,QIAO Kangjia,et al.A Constrained Multi-objective Evolutionary Algorithm Based on Competition and Cooperation Multitasking[J].Journal of Zhengzhou University (Engineering Science),2026,47(XX):1-9.[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XX
页码:
1-9
栏目:
出版日期:
2026-09-10

文章信息/Info

Title:
A Constrained Multi-objective Evolutionary Algorithm Based on Competition and Cooperation Multitasking
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 ;  JING Liang2 ;  QIAO Kangjia2 ;  YUE Caitong2 ;  WANG Xilu3
1. College of Energy and Intelligent Engineering, Henan College of Animal Husbandry and Economics, Zheng zhou 450046, China; 2. School of Electrical and Information Engineering, Zhengzhou Universit y, 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;  Multitasking;  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，其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，其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和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指标下分别在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%，能够有效解决约束多目标优化问题。
Abstract:
Constrained multi-objective evolutionary algorithm based on multitasking has shortcomings in resource allocation and collaborative optimization, resulting in low effectiveness populations wasting computational resources and underutilized high-quality solution information. Therefore, this paper proposes a constrained multi-objective evolutionary algorithm based on competitive and cooperative multitasking, which includes two main strategies: firstly, a competition-based resource allocation strategy is proposed, which achieves 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 is designed, which generates high-quality offspring through cross-task cooperation and spreads them to various task populations, achieving efficient utilization of effective information. The proposed algorithm is compared with five other advanced algorithms (CMOEA_MS, cDPEA, EMCMO, MTCMO, and CMOEMT) on 38 test functions, and the results show that the proposed algorithm achieves optimal results on 25 and 26 functions under IGD and HV indicators, respectively, and is superior to the compared algorithms on at least 23 and 24 functions, respectively; The proposed algorithm has a feasibility rate of 100% on all functions and can effectively solve constrained multi-objective optimization problems.

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备注/Memo

备注/Memo:
收稿日期:2025-04-21;修订日期:2025-06-18
基金项目: 国家自然科学基金资助项目 ( 61922072, U23A20340 ) ; 河南省自然科学基金优秀青年科学基金项 目(242300421168) ;重庆邮电大学大数据智能计算重点实验室开放基金-重点项目( BDIC-2023-A-007)
通信作者:乔康加(1996— ) ,男,河南焦作人,郑州大学初聘副教授,主要从事约束多目标进化优化理论及应用研究,E-mail: qiaokangjia@yeah.net。
更新日期/Last Update: 2026-01-15