Multi Modal Optimization

Multimodal optimization focuses on finding multiple optimal solutions within complex search spaces, a challenge arising frequently in real-world problems. Current research emphasizes developing algorithms that efficiently locate and maintain diverse sets of optima, often employing evolutionary strategies (like Differential Evolution and CMA-ES), niching techniques, and reinforcement learning to guide the search process. These advancements are crucial for tackling diverse applications, from optimizing shared e-mobility services to improving multispectral image analysis and enhancing multi-modal machine learning, where identifying multiple optimal configurations is essential. The development of robust and efficient multimodal optimization methods is driving progress across various scientific and engineering disciplines.