A new multi-population-based differential evolution
Zhihong Zhang · International Journal of Computing Science and Mathematics · 2015
Differential evolution (DE) is an efficient population–based stochastic search algorithm, which has shown good search abilities on many real–world and benchmark optimisation problems. In this paper, we propose a new multi–population–based DE (MDE) algorithm. In MDE, the original population is divided into multiple subpopulations. For each subpopulation, two DE mutation schemes are alternatives to be conducted. Moreover, a Cauchy mutation operator is utilised to enhance the global search. To verify the performance of MDE, 12 well–known benchmark functions are used in the experiments. Simulation results show that MDE performs better than the standard DE and several other DE variants.