SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems
This paper presents a parallel surrogate-based global optimization method for computationally expensive objective functions that is more effective for larger numbers of processors. To reach this goal, we integrated concepts from multi-objective optimization and tabu search into, single objective, surrogate optimization. Our proposed derivative-free algorithm, called SOP, uses non-dominated sorting of points for which the expensive function has been previously evaluated. The two objectives are the expensive function value of the point and the minimum distance of the point to previously evaluated points. Based on the results of non-dominated sorting, P points from the sorted fronts are selected as centers from which many candidate points are generated by random perturbations. Based on surrogate approximation, the best candidate point is subsequently selected for expensive evaluation for each of the P centers, with simultaneous computation on P processors. Centers that previously did not generate good solutions are tabu with a given tenure. We show almost sure convergence of this algorithm under some conditions. The performance of SOP is compared with two RBF based methods. The test results show that SOP is an efficient method that can reduce time required to find a good near optimal solution. In a number of cases the efficiency of SOP is so good that SOP with 8 processors found an accurate answer in less wall-clock time than the other algorithms did with 32 processors.
- Research Organization:
- Cornell Univ., Ithaca, NY (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- Grant/Contract Number:
- SC000 6791; SC0006791
- OSTI ID:
- 1328968
- Alternate ID(s):
- OSTI ID: 1425456
- Journal Information:
- Journal of Global Optimization, Journal Name: Journal of Global Optimization Vol. 66 Journal Issue: 3; ISSN 0925-5001
- Publisher:
- Springer Science + Business MediaCopyright Statement
- Country of Publication:
- Netherlands
- Language:
- English
Web of Science
Multi-surrogate-based global optimization using a score-based infill criterion
|
journal | September 2018 |
On the choice of the low-dimensional domain for global optimization via random embeddings
|
journal | October 2019 |
Similar Records
Collaborative Project: Building improved optimized parameter estimation algorithms to improve methane and nitrogen fluxes in a climate model
Testing Surrogate-Based Optimization with the Fortified Branin-Hoo Extended to Four Dimensions