Self-adaptive competitive differential evolution for dynamic environments

Competitive Differential Evolution (CDE) is a multi-population Differential Evolution (DE) algorithm for optimization in dynamic environments. As such, the control parameters present in DE, are also present in CDE. This paper investigates incorporation of three approaches to self-adapting control parameters into CDE. A comparative evaluation of the performance of each approach is used to determine the most appropriate self-adaptive model for incorporation into CDE. It is shown that self-adapting control parameters does improve the performance of CDE in several instances of benchmark tests. Experimental evidence is presented that indicates that self-adaptive CDE compares favorably with other approaches in the literature.