A Beta Multi-Objective Whale Optimization Algorithm
Ahlem Aboud, Nizar Rokbani, Adel M. Alimi · 2023
This paper presents a new$\beta$-Multi-Objective Whale Optimization Algorithm,$\beta$-MOWOA. The$\beta$-MOWOA algorithm uses two profiles to control both exploration and exploitation phases based on the beta function. The exploitation processing step follow a narrow beta distribution, while the exploration phase uses a large Gaussian-like beta. The experimental study focused on 13 Dynamic Multi-Objective Optimization Problems (DMOPs). Comparative results are based on the Wilcoxon signed rank and the one-way ANOVA. Results proven the statistical significance of the$\beta$-MOWOA algorithm toward state of art methods for solving DMOPs: 9/13 problems using Inverted General Distance and 10/13 using Hypervolume Difference.