Top-k elites based oppositional differential evolution
Jianming Zhang, Weifeng Pan, Jingjing Wu, Jing Wang · International Journal of Wireless and Mobile Computing · 2015
Opposition–based Differential Evolution (ODE) is a new DE variant with faster convergence speed and more robust search abilities than the classical DE. It utilises the concept of opposition and simultaneously evaluates an estimate and its corresponding opposite estimate. Numerical results have shown that the selection of the symmetry point between the estimate and the opposite estimate affects the performance of ODE variants. To make full use of the information included in the elites, this paper presents a novel DE variant, Top–k elites–based Oppositional Differential Evolution (TEODE), which is based on a new opposition–based learning strategy using the top–k elites (TEOBL) in the current generation and employs similar schemes of ODE for population initialisation and generation jumping with TEOBL. Experiments are conducted on 17 benchmark functions. The results confirm that TEODE outperforms classical DE, ODE and COODE (opposition–based differential evolution using the current optimum).