Improved Multi-objective Differential Evolution Algorithm Based on Multi-mutation Individuals
Shen Jia-ji · Jisuanji gongcheng · 2014
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.