An Adaptive Multi-objective Multifactorial Evolutionary Algorithm Based on Mixture Gaussian Distribution

Mengfan Xu, Zexuan Zhu, Yutao Qi, Lei Wang, Xiaoliang Ma

Abstract: In recent decades, multi-objective multifactorial evolutionary algorithm (MOMFEA) has become a very promising research direction. How to achieve effective knowledge transfer between similar tasks is the key issue to affect the performance of the algorithm. In this paper, an adaptive MOMFEA (AMOMFEA) is proposed by exploiting the mixture Gaussian distribution of the population distributions of related tasks to help solve the target task. Wasserstein distance is used to measure the inter-task relevance in that the weight coefficient in the mixture distribution is proportional to the inter-task relevance. Experimental results on benchmark problems validate the effectiveness and efficiency of the proposed method in comparison with MOMFEA and NSGA-II.