A Novel Co-Evolutionary Multi-Objective Optimization Algorithm

To improve the search efficiency of optimization algorithms and solve issues related to local search, this paper proposes a novel cooperative co-evolutionary multi-objective algorithm. Firstly, the estimation of distribution algorithm is used to accelerate the convergence rate to get the optimal so...

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Main Author: ZHU Haifeng
Format: Article
Language:zho
Published: Editorial Office of Control and Information Technology 2025-06-01
Series:Kongzhi Yu Xinxi Jishu
Subjects:
Online Access:http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2025.03.004
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author ZHU Haifeng
author_facet ZHU Haifeng
author_sort ZHU Haifeng
collection DOAJ
description To improve the search efficiency of optimization algorithms and solve issues related to local search, this paper proposes a novel cooperative co-evolutionary multi-objective algorithm. Firstly, the estimation of distribution algorithm is used to accelerate the convergence rate to get the optimal solution, and a "fundamental change" strategy is adopted to improve cooperation between individuals and the evolution of the population, enhancing the global and local search capabilities of the algorithm. Secondly, a straightforward elite-based parent population generation strategy is adopted, which greatly reduces the consumption of computing resources. Through simulation experiments, the results showed that the proposed algorithm improved convergence and distribution indicators by at least 84% and 76% respectively compared to NSGA-II, a classic multi-objective evolutionary algorithm, underscoring its superior search performance.
format Article
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institution Kabale University
issn 2096-5427
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publisher Editorial Office of Control and Information Technology
record_format Article
series Kongzhi Yu Xinxi Jishu
spelling doaj-art-ff94e4d25b5846feb4ccd1e15f4ace122025-08-25T06:57:43ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272025-06-013338117840779A Novel Co-Evolutionary Multi-Objective Optimization AlgorithmZHU HaifengTo improve the search efficiency of optimization algorithms and solve issues related to local search, this paper proposes a novel cooperative co-evolutionary multi-objective algorithm. Firstly, the estimation of distribution algorithm is used to accelerate the convergence rate to get the optimal solution, and a "fundamental change" strategy is adopted to improve cooperation between individuals and the evolution of the population, enhancing the global and local search capabilities of the algorithm. Secondly, a straightforward elite-based parent population generation strategy is adopted, which greatly reduces the consumption of computing resources. Through simulation experiments, the results showed that the proposed algorithm improved convergence and distribution indicators by at least 84% and 76% respectively compared to NSGA-II, a classic multi-objective evolutionary algorithm, underscoring its superior search performance.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2025.03.004multi-objective optimizationco-evolutionaryestimation of distribution algorithm"fundamental change" strategy
spellingShingle ZHU Haifeng
A Novel Co-Evolutionary Multi-Objective Optimization Algorithm
Kongzhi Yu Xinxi Jishu
multi-objective optimization
co-evolutionary
estimation of distribution algorithm
"fundamental change" strategy
title A Novel Co-Evolutionary Multi-Objective Optimization Algorithm
title_full A Novel Co-Evolutionary Multi-Objective Optimization Algorithm
title_fullStr A Novel Co-Evolutionary Multi-Objective Optimization Algorithm
title_full_unstemmed A Novel Co-Evolutionary Multi-Objective Optimization Algorithm
title_short A Novel Co-Evolutionary Multi-Objective Optimization Algorithm
title_sort novel co evolutionary multi objective optimization algorithm
topic multi-objective optimization
co-evolutionary
estimation of distribution algorithm
"fundamental change" strategy
url http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2025.03.004
work_keys_str_mv AT zhuhaifeng anovelcoevolutionarymultiobjectiveoptimizationalgorithm
AT zhuhaifeng novelcoevolutionarymultiobjectiveoptimizationalgorithm