%0 Journal Article
%A DING Qing-feng
%A YANG Liu
%A ZHENG Guo-xin
%T Cellular Differential Evolution Combined Opposition-Based Learning Initialization with Orthogonal Crossover
%D 2014
%R 10.13190/j.jbupt.2014.03.002
%J Journal of Beijing University of Posts and Telecommunications
%P 7-12
%V 37
%N 3
%X <p>A cellular differential evolution (cDE)algorithm based on orthogonal crossover is presented. The opposition-based learning initialization is used to search better solution in the initial stage, the local search within cellular neighbourhood structure is presented to tune the selection pressure instead of the control parameters. And the parallel evolution mechanism of cellular automata is given to ensure the diversity of the evolution population. In addition, the orthogonal crossover is adopted to accelerate the convergence speed with multi-element repeated trials. The performance of the cDE algorithm is evaluated on a suite of classic benchmark functions and compared favorably with the canonical DE and several DE variants. Simulation shows the proposed algorithm has better convergence performance and higher calculation accuracy.</p>
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2014.03.002