Many Objective Optimization

Many-objective optimization tackles the challenge of finding optimal solutions when faced with numerous, often conflicting, objectives. Current research focuses on improving the efficiency and effectiveness of algorithms like NSGA-II, SMS-EMOA, and newer decomposition-based methods, addressing limitations in handling high-dimensional objective spaces and developing robust approaches for diverse problem structures. These advancements are crucial for tackling complex real-world problems across various engineering and scientific domains where multiple performance criteria must be considered simultaneously, leading to better decision-making and optimized designs.