Parallel Multi-objective Optimization using Master-Slave Model on Heterogeneous Resources

File Size Format
51395_1.pdf 284Kb Adobe PDF View
Title Parallel Multi-objective Optimization using Master-Slave Model on Heterogeneous Resources
Author Mostaghim, Sanaz; Branke, Jurgen; Lewis, Andrew; Schmeck, Hartmut
Publication Title IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence).
Editor Michalewicz and Reynolds
Year Published 2008
Place of publication Online
Publisher Online
Abstract In this paper, we study parallelization of multiobjective optimization algorithms on a set of hetergeneous resources based on the Master-Slave model. The Master-Slave model is known to be the simplest parallelization paradigm, where a master processor sends function evaluations to several slave processors. The critical issue when using the standard methods on heterogeneous resources is that in every iteration of the optimization, the master processor has to wait for all of the computing resources (including the slow ones) to deliver the evaluations. In this paper, we study a new algorithm where all of the available computing resources are efficiently utilized to perform the multi-objective optimization task independent of the speed (fast or slow) of the computing processors. For this we propose a hybrid method using Multi-objective Particle Swarm optimization and Binary search methods. The new algorithm has been tested on a scenario contaning heterogeneous resources and the results show that not only does the new algorithm perform well for parallel resources, but also when compared to a normal serial run on one computer
Peer Reviewed Yes
Published Yes
Publisher URI
Alternative URI
Copyright Statement Copyright 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ISBN 978-1-4244-1822-0
Conference name 2008 IEEE World Congress on Computational Intelligence
Location Hong Kong, China
Date From 2008-06-01
Date To 2008-06-06
Date Accessioned 2008-07-09
Language en_AU
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Optimisation
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

Show simple item record

Griffith University copyright notice