Research Repository

TRIOPT: a triangulation-based partitioning algorithm for global optimization

Wu, Yong, Ozdamar, L and Kumar, Arun (2005) TRIOPT: a triangulation-based partitioning algorithm for global optimization. Journal of Computational and Applied Mathematics, 177 (1). pp. 35-53. ISSN 0377-0427

Full text for this resource is not available from the Research Repository.


We propose a triangulation-based partitioning algorithm,TRIOPT, for solving low-dimensional bound-constrained black box global optimization problems. The method starts by forming a Delaunay triangulation of a given set of samples in the feasible domain, and then, it assesses the simplices (partitions) obtained for re-partitioning. Function values at the vertices of each partition are mapped into the zero one interval by a nonlinear transformation function and their aggregate entropy is calculated. Based on this entropy, partitions that hold a promise of containing the global optimum are re-partitioned according to different triangular splitting strategies, forming newpartitions. These strategies are efficient in terms of the number of new function evaluations required per new partition. A novelty in the search scheme proposed here is that once a partition narrows down to a small size, its vertices are eliminated from the available sample set. This changes global information on the best solution and triggers a re-calculation of transformed values. Hence, revised entropies change the direction of the search to new areas. The latter scheme leads to a dynamic parallel search policy which is based on an entropy cut. The tree adopts flexible breadth depending on the status of the search. In the experimental results it is demonstrated that TRIOPTs performance is compatible and often better than that of a well-known response surface methodology and two other efficient black box partitioning approaches proposed for global optimization.

Item Type: Article
Uncontrolled Keywords: ResPubID18514, global optimization, triangulation, partitioning
Subjects: Current > FOR Classification > 0102 Applied Mathematics
Historical > Faculty/School/Research Centre/Department > Institute for Logistics and Supply Chain Management (ILSCM)
Related URLs:
Depositing User: VUIR
Date Deposited: 26 Sep 2011 23:43
Last Modified: 11 Apr 2017 08:50
ePrint Statistics: View download statistics for this item
Citations in Scopus: 7 - View on Scopus

Repository staff only

View Item View Item

Search Google Scholar