Journal
JOURNAL OF COMPUTATIONAL SCIENCE
Volume 5, Issue 1, Pages 12-23Publisher
ELSEVIER
DOI: 10.1016/j.jocs.2013.07.004
Keywords
Particle swarm optimization; Surrogate model; Radial basis function; Expensive function; Groundwater bioremediation; Watershed model calibration
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This paper develops the OPUS (Optimization by Particle swarm Using Surrogates) framework for expensive black-box optimization. In each iteration, OPUS considers multiple trial positions for each particle in the swarm and uses a surrogate model to identify the most promising trial position. Moreover, the current overall best position is refined by finding the global minimum of the surrogate in the neighborhood of that position. OPUS is implemented using an RBF surrogate and the resulting OPUS-RBF algorithm is applied to a 36-D groundwater bioremediation problem, a 14-D watershed calibration problem, and ten mostly 30-0 test problems. OPUS-RBF is compared with a standard PSO, CMA-ES, two other surrogate-assisted PSO algorithms, and an RBF-assisted evolution strategy. The numerical results suggest that OPUS-RBF is promising for expensive black-box optimization. (C) 2013 Elsevier B.V. All rights reserved.
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