Constrained Multiobjective

Constrained multiobjective optimization focuses on finding the best compromise solutions across multiple, often conflicting, objectives, while adhering to limitations or constraints. Current research emphasizes developing robust evolutionary algorithms, including those incorporating techniques like multi-task learning, adaptive tradeoff models, and even large language models to improve convergence speed and solution quality in complex scenarios. This field is crucial for tackling real-world problems across various domains, such as energy system optimization and engineering design, where multiple objectives and constraints are inherent. Improved algorithms are leading to more efficient and effective solutions in these applications.