A study of a non-linear optimization problem using a distributed genetic algorithm

Author

Neves, Nuno ; Nguyen, Anthony-Trung ; Torres, Edgar L.

Author_Institution

Illinois Univ., Champaign, IL, USA

Volume

2

fYear

1996

fDate

12-16 Aug 1996

Firstpage

29

Abstract

Genetic algorithms have been used successfully as a global optimization method when the search space is very large. To characterize and analyze the performance of genetic algorithms on a cluster of workstations, a parallel version of the GENESIS 5.0 was developed using PVM 3.3. This version, called VMGENESIS, was used to study a nonlinear least-squares problem. Performance results show that linear speedups can be achieved if the basic distributed genetic algorithm is combined with a simple dynamic load-balancing mechanism. Results also show that the quality of search changes significantly with the number of processors involved in the computation and with the frequency of communication

Keywords

distributed algorithms; genetic algorithms; least squares approximations; performance evaluation; simulated annealing; GENESIS 5.0; PVM 3.3; VMGENESIS; distributed genetic algorithm; nonlinear least-squares problem; nonlinear optimization problem; performance results; search space; Algorithm design and analysis; Clustering algorithms; Concurrent computing; Dissolved gas analysis; Frequency; Genetic algorithms; Numerical analysis; Optimization methods; Simulated annealing; Workstations;

fLanguage

English

Publisher

ieee

Conference_Titel

Parallel Processing, 1996. Vol.3. Software., Proceedings of the 1996 International Conference on

Conference_Location

Ithaca, NY

ISSN

0190-3918

Print_ISBN

0-8186-7623-X

Type

conf

DOI

10.1109/ICPP.1996.537378

Filename

537378