Multi-factor Optimization Research Using Monte Carlo Simulation and Genetic Algorithm

This study aims to develop and validate a hybrid optimization method that combines Monte Carlo simulation and genetic algorithms to tackle global optimization challenges in complex systems under uncertainty. Traditional optimization techniques often struggle with high-dimensional search spaces and the stochastic nature of real-world problems, especially in agricultural applications where environmental factors and resource constraints add layers of complexity. The proposed method leverages Monte Carlo simulation to generate a diverse set of candidate solutions by effectively handling randomness and uncertainty. These solutions are then refined through genetic algorithms, which enhance convergence and solution quality by iteratively selecting, crossing, and mutating the candidate solutions. Experimental results demonstrate that this hybrid approach efficiently addresses multi-dimensional optimization problems, achieving superior performance compared to conventional methods. It shows significant adaptability and robustness in optimizing crop yield predictions, resource allocation, and scheduling under uncertain conditions. The findings suggest that this hybrid method holds potential for broader applications in various fields, offering a powerful tool for solving complex optimization problems with high accuracy and efficiency.