A diversity-based comparative study for advance variants of differential evolution

Abstract

Differential evolution (DE) is a vector population-based stochastic search optimization algorithm.DEconverges faster, finds the global optimum independent to initial parameters, and uses few control parameters. The exploration and exploitation are the two important diversity characteristics of population-based stochastic search optimization algorithms. Exploration and exploitation are compliment to each other, i.e., a better exploration results in worse exploitation and vice versa. The objective of an efficient algorithm is to maintain the proper balance between exploration and exploitation. This paper focuses on a comparative study based on diversity measures for DE and its prominent variants, namely JADE, jDE, OBDE, and SaDE.