An adaptive elitism-based immigration for grey wolf optimization algorithm

摘要

This paper proposed the adaptive elitism-based immigration to improve the grey wolf optimization performance. The concept of elitism-based immigration is to generated immigrants and replaces it to the worst individuals in the population. The elite immigrants in our proposed are mutated before replace to the worst individuals and the parameter to control mutation ratio and elite immigrants ratio are adaptive. The performances have been evaluated by using 7 well-known benchmark functions and compared with the traditional grey wolf optimizer (GWO) algorithm, particle swarm optimization (PSO) and differential evolution (DE) algorithm. The experimental results showed that the proposed algorithm has ability to solving optimization problems.