Differential evolution algorithm with elite archive and mutation strategies collaboration

This paper proposes a differential evolution algorithm with elite archive and mutation strategies collaboration (EASCDE), wherein two main improvements are presented. Firstly, an elite archive mechanism is introduced to make DE/rand/3 and DE/current-to-best/2 mutation strategies converge faster. Secondly, a mutation strategies collaboration mechanism is developed to tightly combine both strategies to balance global exploration and local exploitation. As a result, EASCDE can effectively keep population diversity in the early stage and significantly enhance convergence speed as well as solution quality in the later stage. The performance of EASCDE is verified by experimental analyses on the well-known test functions. The results demonstrate that EASCDE is superior to other compared competitors in terms of solution precision, convergence speed and stability. Moreover, EASCDE is also an efficient method in dealing with arrival flights scheduling problem.

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