Multimodal Optimization

Multimodal optimization focuses on finding multiple optimal solutions, rather than just a single best solution, within complex search spaces. Current research emphasizes developing algorithms, such as enhanced differential evolution and estimation-of-distribution algorithms, that efficiently locate and maintain diverse sets of high-quality solutions while addressing challenges like redundant exploration and the trade-off between solution diversity and quality. This field is crucial for tackling real-world problems across diverse domains, from engineering design and autonomous systems calibration to parameter estimation in complex models, where multiple optimal solutions offer valuable flexibility and robustness.