With the increase of the number of decision variables in the optimization problem, the problem becomes more and more complex, and when the number of decision variables exceeds a certain value, it is called a largescale multi-objective optimization problem, and it is difficult for traditional algorithms to satisfy the user’s needs. To this end, the fuzzy clustering algorithm is first used to cluster the population particles, and the particle with the highest non-dominated rank is selected as the class globally optimal particle from each class by the non-dominated sorting method, and the class globally optimal solution is applied to the speed updating formula of the multi-objective particle swarm algorithm, which in turn completes the design of the multi-objective optimization problem solution strategy based on the fuzzy clustering-particle swarm combination algorithm, and carries out a Numerical simulation analysis. The IGD values of this paper’s algorithm for the test functions are higher than those of FDEA-II, which outperforms this paper’s algorithm only for three test functions, and the IGD values of RSEA (0.001) are better than those of this paper’s algorithm (0.0037) for MaF2 with M=5, which proves the practicality of the fuzzy clusteringparticle swarm combination algorithm based on fuzzy clustering-particle swarm combination algorithm for the solution strategy in the multi-objective optimization problem reference value.
