Constrained Multi-Objective Optimization with a Limited Budget of Function Evaluations

This paper proposes the Self-Adaptive algorithm for Multi-Objective Constrained Optimization by using Radial Basis Function Approximations, SAMO-COBRA. This 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. SAMO-COBRA is compared to a wide set of other state-of-the-art algorithms (IC-SA-NSGA-II, SA-NSGA-II, NSGA-II, NSGA-III, CEGO, SMES-RBF) on 18 constrained multi-objective problems. In the first experiment, SAMO-COBRA outperforms the other algorithms in terms of achieved Hypervolume (HV) after being given a fixed small evaluation budget on the majority of test functions. In the second experiment, SAMO-COBRA outperforms the majority of competitors in terms of required function evaluations to achieve $$95\%$$95 % of the maximum achievable Hypervolume. In addition to academic test functions, SAMO-COBRA has been applied on a real-world ship design optimization problem with three objectives, two complex constraints, and five decision variables.