Abstract: Differential evolution is a simple and powerful heuristic global optimization algorithm.However,its performance is strongly influenced by the differential evolution strategies and the value of control parameters.Inappropriate strategies and parameters may lead the algorithm fall into premature convergence.Aiming at the problem about selection of strategies and parameters in search process of differential evolution,a population distribution-based self-adaptive differential evolution algorithm was proposed.Firstly,the adaptive factor is established for measuring the distribution of the current population,and the evolution stage of the algorithm can be further determined adaptively.Then,according to the characteristics of different evolution stages,the stage-specific mutation strategies and control parameters are designed,the self-adaptive mechanism is also designed in order to realize dynamic adjustment of strategies and parameters,to balance the global detection and local search capabilityof the algorithm,and improve the search efficiency of the algorithm.Finally,the proposed algorithm is compared with six main-stream differential evolution variants.The numerical experiments of fifteen typical test functions show that the proposed algorithm is superior to six main-stream differential evolution variants in terms of the measures of the average function evaluation times,solution accuracy and converge velocity.Therefore,the computational cost,optimization performance and convergence performance of the proposed algorithm can be proved to be more advantageous.

Key words: Differential evolution, Global optimization, Population distribution, Self-adaptive, Stage division