Multi Objective Optimization Problem

Multi-objective optimization (MOO) tackles problems with multiple, often conflicting, objectives, aiming to find a set of optimal solutions (Pareto front) representing diverse trade-offs. Current research emphasizes efficient algorithms, including evolutionary algorithms (like NSGA-II and MOEA/D), Pareto set learning using neural networks, and novel scalarization techniques, to address challenges in scalability and convergence, particularly for high-dimensional or black-box problems. The ability to effectively balance competing objectives has significant implications across diverse fields, from machine learning model optimization and automated algorithm design to resource allocation in complex systems like energy grids and robotics.