Differential evolution algorithm with ensemble of populations for global numerical optimization

Differential evolution (DE) is an efficient and powerful population-based stochastic search technique for solving global optimization problems over continuous space, which has been widely applied in many scientific and engineering fields. However, the success of DE to handle a specific problem crucially depends on the proper choice of various parameters including the size of the population. Employing the trial and error scheme to search for the most suitable parameter settings requires high computational costs. In this paper, we propose a DE algorithm with an ensemble of parallel populations in which the number of function evaluations allocated to each population is self-adapted by learning from their previous experiences in generating superior solutions. Consequently, a more suitable population size takes most of the function evaluations adaptively to match different phases of the search process/evolution. Although the evolutionary algorithms have been investigated for about five decades, to our best of knowledge so far no effective population adaptation scheme has been proposed. The performance of the DE algorithm with an ensemble of parallel populations is extensively evaluated on a suite of 14 bound-constrained numerical optimization problems and compares favorably with the conventional DE with different single population sizes.