SAMO-COBRA: A Fast Surrogate Assisted Constrained Multi-objective Optimization Algorithm

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