Multi-objective multi-generation Gaussian process optimizer for design optimization

We present a multi-objective optimization algorithm that uses Gaussian process (GP) regression-based models to generate or select trial solutions in a multi-generation iterative procedure. In each generation, a surrogate model is constructed for each objective function with the sample data. The models are used to evaluate solutions and to select the ones with a high potential before they are evaluated on the actual system. Since the trial solutions selected by the GP models tend to have better performance than other methods that only rely on random operations, the new algorithm has much better efficiency in exploring the parameter space. Simulations with multiple test cases show that the new algorithm has a substantially higher convergence speed that the NSGA-II and PSO algorithms.