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A New Framework for Combining Global and Local Methods in Black Box Optimization

Yibo Ji(jiyibo***at***nus.edu.sg)
Sujin Kim(iseks***at***nus.edu.sg)
Lu, Wendy Xu(lu.xu***at***exxonmobil.com)

Abstract: We propose a new framework for the optimization of computationally expensive black box problems, where neither closed-form expressions nor derivatives of the objective functions are available. The proposed framework consists of two procedures. The first constructs a global metamodel to approximate the underlying black box function and explores an unvisited area to search for a global solution; the other identifies a promising local region and conducts a local search to ensure local optimality. To improve the global metamodel, we propose a new method of generating sampling points for a wide class of metamodels, such as kriging and Radial Basis Function models. We also develop a criterion for switching between the global and local search procedures, a key factor affecting practical performance. Under a set of mild regularity conditions, the algorithm converges to the global optimum. Numerical experiments are conducted on a wide variety of test problems from the literature, demonstrating that our method is competitive against existing approaches.

Keywords: Global optimization; simulation optimization; metamodel-based optimization; kriging; radial basis function;

Category 1: Applications -- OR and Management Sciences

Category 2: Global Optimization

Category 3: Nonlinear Optimization (Bound-constrained Optimization )

Citation: Department of Industrial and Systems Engineering, Kent Ridge Crescent, National University of Singapore, Singapore, 119260 July/2013

Download: [PDF]

Entry Submitted: 07/29/2013
Entry Accepted: 07/29/2013
Entry Last Modified: 07/29/2013

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