Parallel surrogate global optimization with paretocenter selection for computationally expensive single objective problems

Tipaluck Krityakierne Institute of Mathematical Statistics and Actuarial Science, University of Bern

Taimoor Akhtar School of Civil and Environmental Engineering, Cornell University, Department of CEE, National University of Singapore

Christine A. Shoemaker School of Civil and Environmental Engineering, Cornell University Department of CEE, National University of Singapore Department of ISE, National University of Singapore

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

The Institute of Operations Research and Analytics (IORA) is a part of NUS’s Smart Nation Research Cluster.

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Parallel surrogate global optimization with paretocenter selection for computationally expensive single objective problems

IORA

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