A model-based approach to multi-objective optimization

We develop a model-based algorithm for the optimization of multiple objective functions that can only be assessed through black-box evaluation. The algorithm iteratively generates candidate solutions from a mixture distribution over the solution space and updates the mixture distribution based on the sampled solutions´ domination count such that the future search is biased towards the set of Pareto optimal solutions. The proposed algorithm seeks to find a mixture distribution on the solution space so that 1) each component of the mixture distribution is a degenerate distribution centered at a Pareto optimal solution and 2) each estimated Pareto optimal solution is uniformly spread across the Pareto optimal set by a threshold distance. We demonstrate the performance of the proposed algorithm on several benchmark problems.