Large-Scale Evolutionary Multi-Objective Optimization Based on Direction Vector Sampling

Abstract

The large-scale multi-objective optimization problem is characterized by a large decision space. How to design an efficient optimization algorithm that can search a large decision space and find the global optimum in the objective space is a very challenging problem at present. In order to solve this problem, this paper proposes a sampling strategy based on direction vectors, which takes into account both convergence and diversity. First, select some excellent individuals who are close to the ideal point based on the reference vector. Secondly, construct a three-way search direction vector using the boundary point and an additional center point, and execute a directional sampling strategy called the convergence-related sampling strategy to improve the convergence of the algorithm. After, the direction vector is constructed among excellent individuals and executes a directional sampling strategy called the diversity-related sampling strategy to maintain the diversity of the population. Finally, the adjustment strategy of the reference vector in the Reference Vector Guidance Algorithm (RVEA) is adopted to adjust the reference vector. Numerical experiments are performed on large-scale multi-objective benchmark problem sets with 500, 1000, and 2000 decision variables and compared with the state-of-the-art algorithms. Experimental results show that the algorithm proposed in this paper is effective and can obtain solutions that are significantly better than those of the compared algorithms.