关键词: 动态差分, 元胞机算法, 多样性维护, 扰动因子, 多目标优化问题

Abstract: To solve the balance difficulty of global search and local optimization for solving high dimensional optimization problems by traditional evolution algorithm,a two-stage dynamic differential agent cellular algorithm based on differential evolution and cellular population topology structure was proposed.By adjusting the individual neighboring structure,the effect of evolutionary population from the structured population to the unstructured population was achieved,and the algorithm could better balance the global search and local optimization.At the same time,the adjustment and complete feedback of the left individuals of the external population improved the convergence speed of the algorithm.The algorithm introduced the agent mechanism into the cellular population,adopted the two-stage external population diversity maintenance method,and introduced a disturbance variable in the mutation operation to avoid the algorithm falling into local optimum.Tests on the WFG series of benchmark functions showed that the new algorithm could obtain a more uniform Pareto front and competitive convergence results when compared to the other four typical algorithms.

Key words: dynamic difference, cellular automata algorithm, diversity maintenance, disturbance variable, multi-objective optimization problems