Abstract: A multi-distribution evolutionary algorithm with differential evolution (MDEA_DE) is proposed by incorporating the strong global convergence of distribution estimation algorithm and the fast convergence of differential evolution. To improve the global convergence ability, MDEA_DE employs a population-based multi-distribution evolution mechanism, and three Gaussian distributions are utilized to generate diverse population with solutions of high quality. Meanwhile, a search space regulation strategy is proposed to improve sampling precision of the Gaussian distributions, and local exploitation ability is enhanced by an improved differential evolution search in the solution space. Experimental results for selected benchmark problems demonstrate that MDEA_DE converges efficiently to the globally optimal solutions of complicated optimization problems by striking a good balance between global exploration and local exploitation.

Key words: distribution evolutionary algorithm, estimation of distribution algorithm (EDA), differential evolution algorithm, Gaussian distribution model, strategy of search space regulation