Multi-surrogates and multi-points infill strategy-based global optimization method

Surrogate-based global optimization (SBGO) methods are widely used to deal with the computationally expensive black-box optimization problems. In order to reduce the computational source, multiple popular individual surrogates containing polynomial response surface (PRS), radial basis functions (RBF), kriging (KRG) and multiple derived ensemble models are constructed to replace the computationally expensive black-box functions. Moreover, a new multi-points infill strategy is presented to accelerate the optimization. New promising points are located by alternately using a hybrid and adaptive promising sampling (HAPS) method and a multi-start sequential quadratic programming (MSSQP) method. The proposed multi-surrogates and multi-points infill strategy-based global optimization (MSMPIGO) method is examined using eighteen unconstrained optimization problems, six nonlinear constrained engineering problems, and one airfoil design optimization problem. Three basic surrogate PRS, RBF, KRG-based global optimization methods using the similar multi-points infill strategy, PRSMPIGO, RBFMPIGO and KRGMPIGO are both considered as the comparative methods. In comparison with PRSMPIGO, RBFMPIGO, KRGMPIGO and three recently introduced SBGO methods, MSMPIGO shows superior search efficiency and strong robustness in locating the global optima.