Abstract

This novel article presents the multi-objective version of the recently proposed the Whale Optimization Algorithm (WOA) known as Non-Dominated Sorting Whale Optimization Algorithm (NSWOA). This proposed NSWOA algorithm works in such a manner that it first collects all non-dominated Pareto optimal solutions in achieve till the evolution of last iteration limit. The best solutions are then chosen from the collection of all Pareto optimal solutions using a crowding distance mechanism based on the coverage of solutions and bubble-net hunting strategy to guide humpback whales towards the dominated regions of multi-objective search spaces. For validate the efficiency and effectiveness of proposed NSWOA algorithm is applied to a set of standard unconstrained, constrained and engineering design problems. The results are verified by comparing NSWOA algorithm against Multi objective Colliding Bodies Optimizer (MOCBO), Multi objective Particle Swarm Optimizer (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II) and Multi objective Symbiotic Organism Search (MOSOS). The results of proposed NSWOA algorithm validates its efficiency in terms of Execution Time (ET) and effectiveness in terms of Generalized Distance (GD), Diversity Metric (DM) on standard unconstraint, constraint and engineering design problem in terms of high coverage and faster convergence.