Abstract:

Differential evolution algorithms are weak in local searching and easy to dropping into the local optimal solutions at the same time. The search performance of these algorithms is mainly based on the parameter setting of their crossover probability and mutation factors. To improve the above shortcomings of differential evolution algorithms, we propose an adaptive differential evolution algorithm called ZJADE on the basis of indepth research and analysis of the adaptive differential evolution with optional external archive (JADE). Skew tent chaotic mapping is used to initialize the population in order to generate uniformly dispersed population. During each generation, the crossover probability of each individual is generated according to the normal distribution and the Cauchy distribution while the mutation factors are independently generated according to the normal distribution. The crossover probability and mutation factors of successful individuals are saved, and the statistical crossover probability is employed. The ZJADE algorithm is compared with multiple stateoftheart adaptive differential evolution algorithms through thirteen classical test functions. The results show that the ZJADE obtains better solution accuracy and quicker convergence speed, thus having a better search performance.

Key words: adaptive differential evolution algorithm;chaotic mapping;statistical crossover probability;Cauchy distribution;normal distribution