Evolutionary Multimodal Optimization

Evolutionary multimodal optimization focuses on finding multiple optimal solutions to complex problems, a significant challenge in many fields. Current research emphasizes improving the efficiency and robustness of algorithms, such as those incorporating diversity maintenance strategies and reinforcement learning to guide the search process, often using particle swarm optimization or genetic algorithms as baselines. These advancements are impacting diverse applications, including model calibration (e.g., in ecological modeling) and resource management (e.g., water quality monitoring), where identifying multiple optimal solutions provides more robust and informative results.