DJADE: a reliable and efficient adaptive differential evolution algorithm with selection pressure control

Our main contribution is the proposition of a new and explicit way of adapting the parental selection pressure for Differential Evolution algorithm (DE) and its experimentation on an adaptive version of differential evolution algorithm named JADE in order to improve its reliability without diminishing its efficiency. The new algorithm named DJADE employs a genotypic population diversity measure (a measure of population convergence) to control the selection pressure parameter. A mechanism that increases the parental selection pressure as the search progresses and in the same time serves as a mean to detect when the algorithm begins to converge (the "convergence phase") is presented. In addition, DJADE is a novel adaptive DE, which adapts crossover, mutation and selection pressure parameters. Moreover, the coefficient K of greediness of the mutation scheme DE/current-to- best/ is also adapted. The motivation for the parameter adaptation is to obtain an algorithm that is capable to handle various problems with different characteristics, notably difficult multimodal and/or nonseparable functions. No case of premature convergence is observed during the experiments conducted on 13 classical functions test at 05 and 30 dimensions. The comparison with classical JADE and Classical DE are presented. DJADE is not only very efficient but also reliable on all functions tested.