Multi Objective Combinatorial Optimization

Multi-objective combinatorial optimization (MOCO) tackles problems with multiple, often conflicting, objectives, aiming to find a set of optimal solutions (the Pareto front) representing the best trade-offs. Current research emphasizes developing efficient algorithms, including neural networks (e.g., pointer networks, reinforcement learning models), and improved metaheuristics that leverage techniques like regret-based elicitation, geometry-aware Pareto set learning, and sparsification methods to enhance solution quality and reduce computational cost. These advancements are crucial for addressing complex real-world problems across diverse fields, such as population synthesis, resource allocation, and pathogen control, where finding optimal solutions considering multiple objectives is essential.