A NEW VERSION OF HYBRID GREY WOLF OPTIMIZER WITH SINE COSINE ALGORITHM (NGWOSCA)

Abstract

In this paper, a new hybridization version of Grey Wolf Optimizer GWO with Sine Cosine Algorithm SCA (NGWOSCA) is proposed. The GWO is a meta-heuristic algorithm inspired from the social hunting behaviour of grey wolves as search agents. The SCA is an optimization technique based on the mathematical inspiration through using sine and cosine functions. Hybridization of algorithms aims to escape from  local minima stagnation and improve the convergence rate and resulting precision by combining strong features of each algorithm. In the proposed algorithm, to improve the solution quality, modifications has been introduced into GWO in three ways. First, improve the exploration ability by transforming convergence factor from linear to nonlinear form. Then, increase the precision by changing the update mechanism of the three leaders alpha, beta and delta. Finally, exploit the advantages and powerful features of GWO and SCA by dividing population into two groups; such group follows one algorithm. The performance of the new algorithm has been evaluated on 23 standard benchmark functions, and the results were compared to GWO and existing hybrid GWO-SCA called (HGWOSCA). Numerical results show that the proposed approach is able to provide very competitive results and outperforms much better than compared algorithms on most tested functions.