Improved Differential Evolution Algorithm based on Dynamic Adaptive Strategies and Control Parameters

To solve the slow convergence speed, low precision in later period and tedious parameter setting of differential evolution when applied to complex optimization functions, an improved differential evolution algorithm (dn-DADE) based on dynamic adaptive strategy is proposed. Firstly, the elite solutions of current population are utilized in the new mutation strategy (DE/current-to-dnbest/1) to guide the search direction, and then these optional elite solutions tend to the global optimal solution in the late stage of evolution to balance the diversity of population and convergence speed. Secondly, the adaptive update strategies of scaling factor and crossover factor are designed for control parameter values self-adapting at different search stages, thus improve the stability and robustness of the algorithm. A set of 14 benchmark functions is 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 several state-of-the-art improved differential evolution algorithms in terms of the main performance indexes.