SAMO-COBRA: a fast surrogate assisted constrained multi-objective optimization algorithm

Abstract

This paper proposes a novel Self-Adaptive algorithm for Multi-Objective Constrained Optimization by using Radial Basis Function Approximations, SAMO-COBRA. The algorithm automatically determines the best Radial Basis Function-fit as surrogates for the objectives as well as the constraints, to find new feasible Pareto-optimal solutions. The algorithm also uses hyper-parameter tuning on the fly to improve its local search strategy. In every iteration one solution is added and evaluated, resulting in a strategy requiring only a small number of function evaluations for finding a set of feasible solutions on the Pareto frontier. The proposed algorithm is compared to a wide set of other state-of-the-art algorithms (NSGA-II, NSGA-III, CEGO, SMES-RBF) on 18 constrained multi-objective problems. In the experiments we show that our algorithm outperforms the other algorithms in terms of achieved Hypervolume after given a fixed small evaluation budget. These results suggest that SAMO-COBRA is a good choice for optimizing constrained multi-objective optimization problems with expensive function evaluations.