Abstract: To solve problems of DE applied to complex optimization functions,an improved differential evolution algorithm (dn-DADE) based on dynamic adaptive strategy was proposed in this paper．Firstly,the elite solutions of current population were utilized in the new mutation strategy (DE/current-to-dnbest/1) to guide the search direction．Secondly,the adaptive update strategies of scaling factor and crossover factor were designed for making parameter values self-adapting at different search stages to improve the stability and robustness of the algorithm．A set of 14benchmark functions were adopted to test the performance of the proposed algorithm．The results show that dn-DADE algorithm has the advantages of remarkable optimizing ability,higher search precision,faster convergence speed and outperforms se-veral state-of-the-art improved differential evolution algorithms in terms of the main performance indexes.

Key words: Differential evolution,Mutation strategy,Dynamic adjustment,Parameter self-adaptation,Global optimization