Design and Development of Grey Wolf Metaheuristic Technique with Application to Combinatorial Optimization Problem

Abstract

Classical algorithms are unable to find the optimal solution for many real life problems. Metaheuristic algorithms were developed to solve these problems. These are popular due to their easy implementation and flexible nature. These algorithms are inspired from behaviour of bird/animals, collective intelligence of swarms, logical behaviour of physical processes occurred in the nature. Grey Wolf Algorithm (GWA) is one of the recent developed meta-heuristic techniques and has been used for solving many optimization problems. The main aim of this dissertation is to develop an improved version of classical Grey Wolf Algorithm (GWA). GWA is inspired from social leadership and hunting mechanism of grey wolves. The two control parameters namely A and C are greatly affect the performance of GWA. The improvement has been done in GWA for further exploration and exploitation. The proposed algorithm is tested on thirteen benchmark functions and a comparative analysis is made with other well-known metaheuristic algorithms.