A New Sequential Approximate Optimization Approach Using Radial Basis Functions for Engineering Optimization

For most engineering optimization problems, it is difficult to find the global optimum due to the unaffordable computational cost. To overcome this difficulty, a new sequential approximate optimization approach using radial basis functions is proposed to find the global optimum for engineering optimization. In the approach, the metamodel is constructed repeatedly to replace the expensive simulation analysis through the addition of sampling points, namely, extrema points of response surface and minimum point of density function. Optimization algorithms simulated annealing and sequential quadratic programming are employed to obtain the final optimal solution. The validity and efficiency of the proposed approach are tested by studying several mathematic examples and one engineering optimization problem.