Abstract:

The large numbers of fitness function evaluation are needed when the engineering optimization problems with time consuming fitness evaluations are solved using a bionic intelligent optimization algorithm. This poses a serious impediment to the field of the bionic intelligent optimization algorithm for the unacceptable high cost of calculation. A cooperative optimization algorithm based on particle swarm optimization (PSO) algorithm and Gaussian process (GP) machine learning for solving computationally expensive optimization problems is presented. GP is used as a surrogate of the real fitness function to prevent frequent fitness function evaluation and predict the most promising solutions before searching the global optimum solution using PSO during each iteration step. The results of study show that the proposed algorithm is much more economical to achieve reasonable accuracy with much less fitness evaluations when solving the optimization problems of the benchmark functions compared with the basic PSO. The proposed algorithm seems very promising to solve the timeconsuming optimization problems.