Chaotic-based grey wolf optimizer for numerical and engineering optimization problems

Abstract

Grey wolf optimizer (GWO) is a recently proposed optimization algorithm inspired from hunting behavior of grey wolves in wild nature. The main challenge of GWO is that it is easy to fall into local optimum. Owing to the ergodicity of chaos, this paper incorporates the chaos theory into the GWO to strengthen the performance of the algorithm. Three different chaotic strategies with eleven various chaotic map functions are investigated and the most suitable one is regarded as the proposed chaotic GWO. Extensive experiments are made to compare the proposed chaotic GWO against other metaheuristics including adaptive differential evolution (JADE), cellular genetic algorithm, artificial bee colony, evolutionary strategy, biogeography-based optimization, comprehensive learning particle swarm optimization, and GWO. In addition, the proposal is also successfully applied to practical engineering problems. Experimental results demonstrate that the chaotic GWO is better than its compared metaheuristics on most of test problems and engineering optimization problems.