Evolutionary Multi-objective Whale Optimization Algorithm

Abstract

Whale Optimization Algorithm (WOA) is a recently proposed metaheuristic algorithm and achieved much attention of the researchers worldwide for its competitive performance over other popular metaheuristic algorithms. As a metaheuristic algorithm, it mimics the hunting behavior of humpback whale which uses its unique spiral bubble-net feeding maneuver to search and hunt prey. The WOA has been designed to solve mono-objective problems and it shows great performance and even surplus other state of the art metaheuristics in terms of fast convergence and other performance criteria. But this such a distinctive and successful metaheuristic’s performance in dealing multi-objective problems especially in dealing with multi-objective benchmark problems has not been studied that much extent. In this paper, we developed a multi-objective version of WOA which incorporates both whale search and evolutionary search strategy. The obtained results are also compared with NSGA-II, NSGA-III, MOEA/D, MOEA/D-DE, MOPSO and d-MOPSO state of art multi-objective evolutionary algorithms.