基于参数动态调整的多目标差分进化算法

Adaptive multi-objective differential evolution algorithm based on the dynamic parameters adjustment

针对多目标差分进化算法最优解难以获取的问题,提出一种基于参数动态调整的多目标差分进化(AMODE)算法.AMODE算法通过设计变异率和交叉率的自适应调整策略,实现进化过程中变异率和交叉率的动态调整,均衡多目标差分进化算法的局部搜索能力和全局探索能力,获得收敛性、多样性和均匀性较好的最优解.实验结果表明,基于参数动态调整的AMODE算法能够有效改善多目标差分进化算法的逼近能力(IGD)和均匀性(SP),具有较好的优化效果.

To obtain the optimal solutions of the multi-objective differential evolution algorithm, an adaptive multiobjective differential evolution（AMODE） algorithm based on the dynamic parameters adjustment strategies is developed,in which the adaptive adjustment strategies are designed to select the scaling factor and crossover rate. Then, the suitable scaling factor and crossover rate can be calculated in the mutation and crossover processes to balance the local search and the global exploration abilities of the multi-objective differential evolution algorithm. Thus, the integrity and uniformity optimal solutions are able to be obtained in the evolutionary process. The experimental results show that this proposed AMODE algorithm has a better effect to improve the inverted generational distance（IGD） and spacing（SP）.