An efficient method for parallel interval global optimization

Author

Baldwin, Adam ; Asaithambi, Asai

Author_Institution

Comput. Sci. Dept., Univ. of South Dakota, Vermillion, SD, USA

fYear

2011

fDate

4-8 July 2011

Firstpage

317

Lastpage

321

Abstract

Finding the global minimum for an arbitrary differentiable function over an n-dimensional rectangle is an important problem in computational science, with applications in many disciplines. We present a parallel depth-first algorithm along with a potential load balancing technique, and acceleration devices that provides a significant reduction in run time compared with a popular breadth-first search algorithm. Our algorithm reliably obtains global minima for test functions commonly used in the literature, with the highest speedup achieved for highly multimodal functions.

Keywords

mathematics computing; optimisation; resource allocation; search problems; arbitrary differentiable function; breadth first search algorithm; computational science; global minima; load balancing technique; n-dimensional rectangle; parallel depth first algorithm; parallel interval global optimization; test functions; Acceleration; Algorithm design and analysis; Clustering algorithms; Load management; Optimization; Program processors; Reliability; Global optimization; acceleration method; depth-first; interval analysis; parallel algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

High Performance Computing and Simulation (HPCS), 2011 International Conference on

Conference_Location

Istanbul

Print_ISBN

978-1-61284-380-3

Type

conf

DOI

10.1109/HPCSim.2011.5999840

Filename

5999840