• A Novel Co-Evolutionary Multi-...

Treffer: A Novel Co-Evolutionary Multi-Objective Optimization Algorithm

Title:
A Novel Co-Evolutionary Multi-Objective Optimization Algorithm
Authors:
ZHU Haifeng
Source:
Kongzhi Yu Xinxi Jishu, Pp 33-38 (2025)
Publisher Information:
Editorial Office of Control and Information Technology, 2025.
Publication Year:
2025
Collection:
LCC:Technology
Subject Terms:
multi-objective optimization, co-evolutionary, estimation of distribution algorithm, "fundamental change" strategy, Control engineering systems. Automatic machinery (General), TJ212-225, Technology
Document Type:
Fachzeitschrift article
File Description:
electronic resource
Language:
Chinese
ISSN:
2096-5427
Relation:
http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2025.03.004; https://doaj.org/toc/2096-5427
DOI:
10.13889/j.issn.2096-5427.2025.03.004
Access URL:
https://doaj.org/article/ff94e4d25b5846feb4ccd1e15f4ace12
Accession Number:
edsdoj.ff94e4d25b5846feb4ccd1e15f4ace12
Database:
Directory of Open Access Journals

Weitere Informationen

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.