A Short Survey of Evolutionary Algorithms for Solving Many Objective Optimization Problems

Multi-objective evolutionary algorithms have been shown to solve multi-objective optimization problems well and have been very widely used, but there are still drawbacks such as failure to develop sufficient environmental selection pressure to guide the population search toward the Pareto front, long computation time of the algorithms, and difficulty in visualization in the face of MaOPs, which have led to an increasing interest in the research of solving MaOPs. In addition, there are many expensive MaOPs in many practical applications of MaOPs. This paper has the following three parts of work: introducing common mainstream methods for solving MaOPs; introducing common surrogate-assisted methods for solving expensive MaOPs; applying MaOEAs and surrogate-assisted evolutionary algorithms to solve different MaOPs based on platEMO. The results show that there are differences in the effectiveness of different methods in solving different problems.