Multi-objective multi-generation Gaussian process optimizer for design optimization
This work addresses efficiency challenges in design optimization for engineering or computational domains, representing an incremental improvement over prior evolutionary algorithms.
The paper tackles the problem of multi-objective design optimization by introducing a multi-generation evolutionary algorithm that uses Gaussian process regression models to select trial solutions, resulting in substantially higher convergence speed and stability compared to existing methods like NSGA-II and MOPSO.
We present a multi-objective evolutionary optimization algorithm that uses Gaussian process (GP) regression-based models to 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 higher efficiency in exploring the parameter space. Simulations with multiple test cases show that the new algorithm has a substantially higher convergence speed and stability than NSGA-II, MOPSO, and some other more recent algorithms.