Adaptive differential evolution algorithm based on Nth-order nearest-neighbor analysis
Li Juan Yu · Control theory & applications · 2011
To improve the reliability of differential evolution(DE) algorithm in dealing with multimodal optimiza-tion problem,we propose an adaptive differential evolution(ADE) algorithm based on the Nth-order nearest-neighbor analysis(N--NNADE).Global distribution information of species is obtained by analyzing the Nth-order nearest-neighbor in population,and the number of species is adaptively determined by the step-jumping information in lacking prior knowl-edge,Furthermore,the K--means algorithm is used for partitioning the population.To realize the co-evolution among species,we introduce the crossover mutation among different species and replace the worst members of current species if parents and children are belonging to the same species.The reliability of the algorithm is continuously improved by acquir-ing more global optimal solutions and high-quality local suboptimal solutions.The results of 20 benchmark optimization problems show that N--NNADE algorithm is more suitable than DE,DERL(differential evolution algorithm with random localizations) and ADE for solving complex high-dimensional multimodal optimization problems