A Beta Multi-Objective Whale Optimization Algorithm

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.