Research on Multimodal Optimization Mechanism based on Machine Learning

As modern engineering optimisation problems become increasingly complex, a significant number of them exhibit multimodal characteristics. This implies that there are multiple local optimal solutions and a single global optimal solution. This not only increases the difficulty of problem-solving, but also necessitates the development of more sophisticated algorithms for optimisation. In this work, by constructing intelligent agents to continuously optimize their behavior strategies in the search process of multimodal optimization problems, they use environmental feedback to continuously optimize their behavior strategies, and at the same time simulate the process of natural selection and genetic variation to generate multiple candidate solutions, so as to conduct extensive searches on a global scale. Deep neural networks are used to build policy networks and value networks for reinforcement learning agents, enabling agents to accurately predict the potential benefits of different search strategies through a large amount of training data. Through real-time monitoring and analysis of feedback information, the model dynamically adjusts the search strategy to balance exploration and utilization, avoid falling into the local optimal solution, and finally find the global optimal solution. Experimental analysis shows that the model performs well in dealing with complex multimodal optimization problems, and significantly improves the efficiency and effect of solving.