An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization

Abstract

Grey wolf optimizer (GWO) algorithm is a relatively novel population-based optimization technique that has the advantage of less control parameters, strong global optimization ability and easy of implementation. It has received significant interest from researchers in different fields. However, there is still an insufficiency in the GWO algorithm regarding its position-updated equation, which is good at exploitation but poor at exploration. In this work, we proposed an improved algorithm called the exploration-enhanced GWO (EEGWO) algorithm. In order to improve the exploration, a new position-updated equation is presented by applying a random individual in the population to guide the search of new candidate individuals. In addition, in order to make full use of and balance the exploration and exploitation of the GWO algorithm, we introduced a nonlinear control parameter strategy, i.e., the control parameter of a→ is nonlinearly increased over the course of iterations. The experimental result on a set of 23 benchmark functions and 4 engineering applications demonstrate the effectiveness and efficiency of the modified position-updated equation and the nonlinear control parameter strategy. The comparisons show that the proposed EEGWO algorithm significantly improves the performance of GWO. Moreover, EEGWO offers the highest solution quality, strongest robustness, and fastest global convergence among all of the contenders on almost all of the test functions.