关键词: 差分进化算法, 多目标优化, 自适应动态变异, 二次变异, 种群多样性

Abstract: In order to keep balance between diversity and convergence of Differential Evolution (DE) in solving multiobjective optimization, an improved DE based on adaptive dynamic mutation and second mutation of non-dominance solution was proposed. The core of this algorithm was： a new diversity dominance based on the previous population diversity was presented so that DE could adaptively select the corresponding mutation function for generating new individual according to the diversity variety. In addition, second mutation based on Pareto non-dominance solution archive was executed in order to improve quantity and quality of new individual solutions, algorithm's diversity and convergence speed were improved simultaneously. Compared to standard DE and non-dominated sorting genetic algorithm II，simulation results showed that this algorithm had better convergence, diversity and spread, and it was also superior to other two algorithms in keeping balance between diversity and convergence.

Key words: differential evolution algorithm, multiobjective optimization, self-adaptive dynamic mutation, second mutation, population diversity