2019/06/29 by Xiaobiao Huang, Huang, Xiaobiao, Minghao Song +3
Computer Science · Decision Sciences · Engineering · Physics and Astronomy · #Accelerator Physics (physics.acc-ph) #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #FOS: Physical sciences #Heat Transfer and Optimization #Neural and Evolutionary Computing (cs.NE) #Optimal Experimental Design Methods #cs.NE #physics.acc-ph
paper · pdf · doi:10.48550/arxiv.1907.00250
openalex publication_date 2019/06/29 · arxiv created 2020/05/21 · arxiv updated 2020/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.