Expensive Multi Objective Optimization

Expensive multi-objective optimization (EMO) tackles problems where finding the best compromise among multiple, computationally costly objectives is crucial. Current research focuses on improving the efficiency of surrogate-assisted evolutionary algorithms and Bayesian optimization methods, often employing techniques like Gaussian processes, diffusion models, and various neural network architectures to build accurate and reliable approximations of the Pareto front. These advancements are vital for addressing real-world challenges in diverse fields like engineering design and scientific modeling where exhaustive exploration of the solution space is infeasible due to high computational costs. The ultimate goal is to obtain a representative set of optimal solutions with minimal function evaluations, enabling faster and more informed decision-making.