Dynamic mode-pursuing sampling method for black-box function optimization problems

To deal with the expensive black-box function optimization problems in the complex industrial design processes, an improved Dynamic Mode-Pursuing Sampling (DMPS) method was presented. The linear spline function was used for global approximation. Through random sampling process, the design points of approximate global optimal areas were generated, and quadratic regression function was performed to judge the global convergence. For improving the global search capability and function adaptability, a new dynamic acceleration factor was introduced. The modified multiple correlation coefficients were used to update the acceleration factor and to judge the fitting accuracy of response surface. Simulation results on standard test functions showed that DMPS method had better ability for finding global optimum and reducing the function evaluation times compared with genetic algorithm and simulated annealing algorithm.