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Experimental Study on Recent Advances in Differential Evolution Algorithm
Abstract
The Differential Evolution (DE) is a well known Evolutionary Algorithm (EA), and is popular for its simplicity. Several novelties have been proposed in research to enhance the performance of DE. This paper focuses on demonstrating the performance enhancement of DE by implementing some of the recent ideas in DE’s research viz. Dynamic Differential Evolution (dDE), Multiple Trial Vector Differential Evolution (mtvDE), Mixed Variant Differential Evolution (mvDE), Best Trial Vector Differential Evolution (btvDE), Distributed Differential Evolution (diDE) and their combinations. The authors have chosen fourteen variants of DE and six benchmark functions with different modality viz. Unimodal Separable, Unimodal Nonseparable, Multimodal Separable, and Multimodal Nonseparable. On analyzing distributed DE and mixed variant DE, a novel mixed-variant distributed DE is proposed whereby the subpopulations (islands) employ different DE variants to cooperatively solve the given problem. The competitive performance of mixed-variant distributed DE on the chosen problem is also demonstrated. The variants are well compared by their mean objective function values and probability of convergence.
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