摘要： 针对多目标差分进化算法在高维函数下收敛速度慢和易早熟的问题，提出一种基于多变异个体的多目标差分进化改进算法。通过在多目标差分进化算法的个体变异及交叉操作中，引入多个变异个体，使得在高维多目标函数情况下，多目标差分进化算法种群可以更好地保持多样性，减少种群陷入局部最优解的可能性，从而提高该算法在高维多目标优化问题环境下，最优值解的搜索速度及全局最优值解的查找能力。实验结果表明，在高维多目标环境下，与标准多目标差分进化算法相比，该算法可以更快速地找到多个目标函数组的非劣最优值解集。

关键词: 多目标优化问题, 差分进化算法, 多变异个, 计算智能, 最优值搜索, 迭代速度

Abstract: Aiming to the problem of multi-objective Differential Evolution(DE) algorithms which have the characteristics of prematurity and slow convergence speed under high-dimensional situation, this paper proposes an improved multi-objective DE algorithms based on multi-mutation samples. Through using method of introducing multi-mutation individuals into the mutation operator and crossover operator of multi-objective DE algorithm, multi-objective DE algorithm populations can keep diversity, reduce the possibility of falling into local optimal solution, it has guick speed for optimal solution, and the improves the ability finding optimal solution using shorter iteration steps than standard multi-objective differential evolution algorithm. Experimental results show that compared with standarded multi-objective DE algorithms, the improved algorithm can find optimal value effectively in high-dimensional multi-objective environment.

Key words: multi-objective optimization problem, Differential Evolution(DE) algorithm, multi-mutation individuals, computational intelligence, optimal value searching, iteration speed

中图分类号: 

• TP18