A Hybrid Surrogate-Assisted Accelerated Random Search and Trust Region Approach for Constrained Black-Box Optimization

Abstract

This paper presents a hybrid surrogate-based approach for constrained expensive black-box optimization that combines RBF-assisted Constrained Accelerated Random Search (CARS-RBF) with the CONORBIT trust region method. Extensive numerical experiments have shown the effectiveness of the CARS-RBF and CONORBIT algorithms on many test problems and the hybrid algorithm combines the strengths of these methods. The proposed CARS-RBF-CONORBIT hybrid alternates between running CARS-RBF for global search and a series of local searches using the CONORBIT trust region algorithm. In particular, after each CARS-RBF run, a fraction of the best feasible sample points are clustered to identify potential basins of attraction. Then, CONORBIT is run several times using each cluster of sample points as initial points together with infeasible sample points within a certain radius of the centroid of each cluster. One advantage of this approach is that the CONORBIT runs reuse some of the feasible and infeasible sample points that were previously generated by CARS-RBF and other CONORBIT runs. Numerical experiments on the CEC 2010 benchmark problems showed promising results for the proposed hybrid in comparison with CARS-RBF or CONORBIT alone given a relatively limited computational budget.