GFE date

In large-scale multi-objective optimization, as the decision space's dimensionality increases, evolutionary algorithms can easily fall into an optimal local state. Therefore, how to prevent the algorithm from falling into a local optimum and quickly converge to the Pareto front is a particularly challenging problem. In order to solve the problem, this paper proposes a grid-based fuzzy evolution large-scale multi-objective optimization framework, which divides the entire evolution process into two main stages: fuzzy evolution and precise evolution. In the first stage, many similar original solutions will be fuzzy into the same fuzzy solution. The purpose is to enhance the population's solutions so fuzzy evolution can prevent the algorithm from falling into the local optimum. The second stage aims to find a high-precision solution so that it is closer to the true Pareto front. This paper conducts experiments on various large-scale multi-objective problems with as many as 500 to 5000 decision variables. Experimental results show that in large-scale multi-objective optimization, the framework proposed in this paper can significantly improve the performance and computational efficiency of multi-objective optimization algorithms.