Accelerated Random Search for constrained global optimization assisted by Radial Basis Function surrogates
作者：
Highlights：
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• Developed extension of Accelerated Random Search (ARS) to constrained optimization.
• Proved convergence of more general class of Scattered Uniform Random Search algorithms.
• Constrained ARS (CARS) compares favorably with alternatives on 31 benchmark problems.
• Radial basis function (RBF) surrogates approximate objective and constraints in CARS.
• CARS with RBF is an improvement over CARS and is competitive with alternatives.
摘要
•Developed extension of Accelerated Random Search (ARS) to constrained optimization.•Proved convergence of more general class of Scattered Uniform Random Search algorithms.•Constrained ARS (CARS) compares favorably with alternatives on 31 benchmark problems.•Radial basis function (RBF) surrogates approximate objective and constraints in CARS.•CARS with RBF is an improvement over CARS and is competitive with alternatives.
论文关键词：Constrained global optimization,Random search,Expensive optimization,Surrogate model,Radial basis function
论文评审过程：Received 25 August 2017, Revised 4 February 2018, Available online 7 March 2018, Version of Record 22 March 2018.
论文官网地址：https://doi.org/10.1016/j.cam.2018.02.017
