Abstract: The decision space dimension of large-scale multi-objective optimization problemsis up to hundreds of dimensions.It is extremely challenging to achieve fast convergence in the huge search space while efficiently maintaining the diversity of the population.To address the above problems,a dual direction vectors-based large-scale multi-objective evolutionary algorithm(DDLE) is proposed in the paper.The main idea of the algorithm is to utilize two different types of direction vectors to guide the population evolution and improve the search efficiency of the algorithm.First,a convergent direction vector generation strategy is designed to improve the convergence speed of the algorithm.Second,a diversity direction vector generation strategy is introduced to enhance the diversity of the population.Finally,an adaptive environment-based selection operator is proposed to dynamically balance the convergence and diversity in the process of population evolution.To verify the performance of DDLE,it is compared with five state-of-the-art algorithms in experiments on 72 large-scale benchmark test problems.Experimental results show that DDLE has a significant advantage over other compared algorithms in solving large-scale multi-objective optimization problems.

Key words: Evolutionary algorithms, Large-scale multi-objective optimization, Dual direction vectors, Convergence direction vector, diversity direction vector