An Adaptive Differential Evolution with Mutation Strategy Pools for Global Optimization

This paper proposes an adaptive differential evolution algorithm with mutation strategy pools for global optimization. In the proposed method, the mutation strategy pool mechanism is devised to supply appropriate mutation strategy for different individuals in the population. Further, a mutation strategy, called DE/current-to-wb/1, has also been designed and employed in the mutation strategy pool. The performance of the proposed algorithm has been evaluated on CEC'2014 benchmark functions and compared with related methods. Experimental results show that the proposed algorithm has a good performance and outperforms related algorithms.