Research on Multi-Objective Optimization Problems Combining Random Forest Method and Genetic Algorithm

As the complexity of modern engineering and scientific problems continues to increase, Multi-Objective Optimization Problems (MOPs) have been widely applied across various fields. However, traditional optimization algorithms often face challenges such as high computational complexity and slow solving speed when addressing multi-objective problems, especially in high-dimensional and computationally expensive scenarios. To address these issues, this paper proposes a hybrid optimization method that combines Non-dominated Sorting Genetic Algorithm II (NSGA-II) with Random Forest (RF), aimed at improving solving efficiency and optimization accuracy. By introducing the Random Forest model into the search process of the genetic algorithm, the method uses it to predict and quickly evaluate the fitness of individuals, thereby reducing unnecessary computations and significantly enhancing the computational efficiency of the optimization process. Extensive experiments conducted in this study demonstrate the effectiveness and superiority of this method in solving high-dimensional multi-objective optimization problems, especially when computational resources are limited.