Multi-Objective Optimization Algorithm under Improved SCAM for Pseudo Physical Optical Constraints

The mimetic physics optimization algorithm is an efficient random search method based on population. This article conducted simulation experiments on the multi-objective fitting physical optimization method. This article focuses on high-dimensional multi-objective optimization problems, based on the mechanism of pseudo physical optimization, and studies high-dimensional multi-objective pseudo physical optimization methods. This article studies its differences on the Pareto non inferior solution set, establishes a multi-objective pseudo physical optimization algorithm based on virtual force order, and theoretically analyzes and demonstrates its convergence on this basis. The average running time of the simulated physical optical constrained multi-objective optimization algorithm based on improved SCAM (Stanford Copy Analysis Mechanism) is 7.06 seconds, while the average running time of the CRMOAPO (constrained rank multi-objective APO) algorithm is 11.45 seconds. Through simulation experiments and comparison with traditional multi-objective evolutionary algorithms, the results show that the method proposed in this paper can better preserve the diversity of the solution set.