A two‐stage many‐objective evolutionary algorithm with dynamic generalized Pareto dominance. Issue 11 (29th August 2022)

• Record Type: Journal Article
• Authors: Wang, Hui; Wei, Zichen; Yu, Gan; Wang, Shuai; Wu, Jiali; Liu, Jiawen
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 11(2022)
• Page Start: 9833
• Page End: 9862
• Publication Date: 2022-08-29
• Subjects: generalized Pareto dominance -- many‐objective evolutionary algorithms -- many‐objective optimization problems -- multiobjective optimization -- two‐stage selection; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.23016
• Languages: English
• ISSNs: 0884-8173

Abstract

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 L p ${L}_{p}$ ‐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.