Multimodal Multi Objective

Multimodal multi-objective optimization (MMOO) tackles problems with multiple optimal solutions (both globally and locally) across multiple objective functions. Current research focuses on developing efficient algorithms, such as coevolutionary frameworks and Bayesian optimization approaches, to locate these diverse optimal sets, often employing techniques like gradient sliding or Bézier curve parameterization to improve solution navigation and representation. The ability to effectively identify and analyze multiple optimal solutions is crucial for numerous real-world applications where a single "best" solution may not be feasible or desirable, leading to improved decision-making in engineering, design, and other fields.