The purpose of this paper is to improve the efficiency of particle optimization method by using direct and indirect surrogate modeling in inverse design problems.
The new algorithm emphasizes the use of a direct and an indirect design prediction based on local surrogate models in particle swarm optimization (PSO) algorithm. Local response surface approximations are constructed by using radial basis neural networks. The principal role of surrogate models is to answer the question of which individuals should be placed into the next swarm. Therefore, the main purpose of surrogate models is to predict new design points instead of estimating the objective function values. To demonstrate its merits, the new approach and six comparative algorithms were applied to two different test cases including surface fitting of a geographical terrain and an inverse design of a wing, the averaged best-individual fitness values of the algorithms were recorded for a fair comparison.
The new algorithm provides more than 60 per cent reduction in the required generations as compared with comparative algorithms.
The comparative study was carried out only for two different test cases. It is possible to extend test cases for different problems.
The proposed algorithm can be applied to different inverse design problems.
The study presents extra ordinary application of double surrogate modeling usage in PSO for inverse design problems.
