2020/09/04 by Giacomo Nannicini, Nannicini, Giacomo
Computer Science · Decision Sciences · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Advanced Optimization Algorithms Research #Discrete Mathematics (cs.DM) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Probabilistic and Robust Engineering Design
paper · pdf · doi:10.48550/arxiv.2009.02183
openalex publication_date 2020/09/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We describe the optimization algorithm implemented in the open-source\nderivative-free solver RBFOpt. The algorithm is based on the radial basis\nfunction method of Gutmann and the metric stochastic response surface method of\nRegis and Shoemaker. We propose several modifications aimed at generalizing and\nimproving these two algorithms: (i) the use of an extended space to represent\ncategorical variables in unary encoding; (ii) a refinement phase to locally\nimprove a candidate solution; (iii) interpolation models without the\nunisolvence condition, to both help deal with categorical variables, and\ninitiate the optimization before a uniquely determined model is possible; (iv)\na master-worker framework to allow asynchronous objective function evaluations\nin parallel. Numerical experiments show the effectiveness of these ideas.\n