A hybrid differential evolution with grey wolf optimizer for continuous global optimization

This paper proposes a hybrid differential evolution algorithm with grey wolf optimizer for solving continuous global optimization problems. The proposed algorithm introduces a new improved mutation schemes. In this algorithm, the control parameters are self-adapted by learning from previous evolutionary search. Beside, the grey wolf optimizer algorithm is used to enhance the crossover strategy. The performance of the proposed algorithm was evaluated on nine well-known benchmark functions and it was compared to particle swarm optimization, the traditional differential evolution algorithm and the self-adaptive differential evolution algorithm (jDE). The experimental results suggested that the proposed algorithm performed effectively to solving complex optimization problems.