GREY WOLF OPTIMIZATION USING ENHANCED MUTATION OPPOSITIONAL BASED LEARNING FOR OPTIMIZATION PROBLEMS

Abstract

Grey wolf optimization (GWO) algorithm is a swarm intelligence optimization technique that is developed by Mirjalili [1] to mimic the hunting behavior and leadership hierarchy of grey wolves in nature. It has been successfully applied to many real site applications. However, there is still an insufficiency in the GWO algorithm regarding its position-updated equations and fewer operators, which is good at exploitation but poor at exploration and easy to trap in the local optima.

Oppositional-based learning (OBL) is a new concept, which has attracted many research efforts in the last decade, some optimization methods have already used the concept of OBL to improve their performance [2].

This paper presents an efficient algorithm, namely, Enhanced Mutation Oppositional-based learning GWO (EMOGWO) based on Quasi Oppositional-based learning (QOBL) and Topological Oppositional-based learning (TOBL) with some parameter adjustment to balance between exploration and exploitation.

The experiments were executed 28 widely used benchmark test functions with various features. The results reveal that the proposed algorithm shows better or at least competitive performance against other compared algorithms in terms of exploration and exploitation.