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A two‐stage many‐objective evolutionary algorithm with dynamic generalized Pareto dominance.

Authors :
Wang, Hui
Wei, Zichen
Yu, Gan
Wang, Shuai
Wu, Jiali
Liu, Jiawen
Source :
International Journal of Intelligent Systems; Nov2022, Vol. 37 Issue 11, p9833-9862, 30p
Publication Year :
2022

Abstract

Many‐objective evolutionary algorithms (MaOEAs) are widely used to solve many‐objective optimization problems. As the number of objectives increases, it is difficult to achieve a balance between the population diversity and the convergence. Additionally, the selection pressure decreases rapidly. To tackle these issues, this paper proposes a two‐stage many‐objective evolutionary algorithm with dynamic generalized Pareto dominance (called TS‐DGPD). First, a two‐stage method is utilized for environmental selection. The first stage employs the cosine distance to accelerate the convergence. The second stage uses Lp ${L}_{p}$‐norm maintain the population diversity. Moreover, a dynamic generalized Pareto dominance (DGPD) is used to increase the selection pressure of the population. To evaluate the performance of TS‐DGPD, we compare it with several other MaOEAs on two benchmark sets with 3, 5, 8, 10, 15, and 20 objectives. Experimental results show that TS‐DGPO performs satisfactorily on convergence and diversity. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08848173
Volume :
37
Issue :
11
Database :
Complementary Index
Journal :
International Journal of Intelligent Systems
Publication Type :
Academic Journal
Accession number :
159361804
Full Text :
https://doi.org/10.1002/int.23016