An improved differential evolution algorithm with dual mutation strategies collaboration

Abstract To reduce the effect of the selections of mutation strategies and control parameters on the performance of differential evolution (DE), this paper proposes an improved differential evolution algorithm with dual mutation strategies collaboration (DMCDE), in which two main improvements are presented. First, DMCDE introduces an elite guidance mechanism to propose two new variants of the classical DE/rand/2 and DE/best/2 mutation strategies, which we call DE/e-rand/2 and DE/e-best/2 respectively. They use the individuals randomly chosen from superior elite population as the base vector and the first vector of difference vectors, thereby providing clearer guidance for individual mutation without losing randomness. Second, a mechanism of dual mutation strategies collaboration is utilized to obtain a trade-off between global exploration and local exploitation of the algorithm. The performance of DMCDE is evaluated by using the commonly used test functions as well as a real-world optimization problem. The results show that DMCDE can significantly improve the optimization performance of DE, and is superior to the comparative competitors.

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