SOP: Parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems

Krityakierne, Tipaluck; Akhtar, Taimoor; Shoemaker, Christine A. (2016). SOP: Parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems. Journal of global optimization, 66(3), pp. 417-437. Springer 10.1007/s10898-016-0407-7

art%3A10.1007%2Fs10898-016-0407-7.pdf - Published Version
Available under License Creative Commons: Attribution (CC-BY).

Download (968kB) | Preview

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.

Item Type:

Journal Article (Original Article)


08 Faculty of Science > Department of Mathematics and Statistics > Institute of Mathematical Statistics and Actuarial Science

UniBE Contributor:

Krityakierne, Tipaluck


500 Science > 510 Mathematics








Lutz Dümbgen

Date Deposited:

07 Apr 2016 11:17

Last Modified:

13 Mar 2021 20:09

Publisher DOI:





Actions (login required)

Edit item Edit item
Provide Feedback