Parallel combinatorial optimization with evolutionary cooperation between processors

Author

Ortega, J. ; Bernier, J.L. ; Díaz, A.F. ; Rojas, I. ; Salmerón, M. ; Prieto, A.

Author_Institution

Dept. de Arquitectura y Tecnologia de Comput., Granada Univ., Spain

Volume

2

fYear

1999

fDate

1999

Abstract

An evolutionary computation approach is used to learn online the rules that allow the processors in a parallel platform to cooperate by interchanging the local optima that they find while they concurrently explore different zones of the solution space. The cooperation of processors can greatly benefit the resolution of combinatorial optimization problems by decreasing their runtimes, by increasing the quality of the solutions obtained, or both. Moreover, as parallel computers are more and more accessible, the application of parallel processing to solve these problems becomes a practical and interesting alternative. As an example, a parallel optimization algorithm based on Boltzmann Machine has been used for a detailed description and evaluation of the proposed cooperation approach

Keywords

Boltzmann machines; combinatorial mathematics; evolutionary computation; learning (artificial intelligence); parallel algorithms; parallel architectures; Boltzmann Machine; combinatorial optimization problems; cooperation approach; evolutionary computation approach; evolutionary cooperation; local optima; parallel combinatorial optimization; parallel computers; parallel optimization algorithm; parallel platform; parallel processing; solution space; Application software; Computer architecture; Concurrent computing; Costs; Evolutionary computation; Genetic algorithms; Optimization methods; Parallel processing; Runtime; Workstations;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on

Conference_Location

Washington, DC

Print_ISBN

0-7803-5536-9

Type

conf

DOI

10.1109/CEC.1999.782539

Filename

782539