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

Abstract This paper proposes the Self-Adaptive algorithm for Multi-Objective Constrained Optimization by using Radial Basis Function Approximations, SAMO-COBRA. automatically determines best Function-fit as surrogates objectives well constraints, to find new feasible Pareto-optimal solutions. SAMO-COBRA is compared 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 first experiment, outperforms in terms achieved Hypervolume (HV) after being given fixed small evaluation budget majority test functions. second competitors required function evaluations achieve $$95\%$$ 95 % maximum achievable Hypervolume. addition academic functions, has been applied real-world ship design optimization problem with three objectives, two complex and five decision variables.