Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22257
Title: Performance of particle swarm optimization in scheduling hybrid flow-shops with multiprocessor tasks
Authors: Ercan, MF
Fung, YF
Issue Date: 2007
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2007, v. 4707 lncs, no. part 3, p. 309-318 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: In many industrial and computing applications, proper scheduling of tasks can determine the overall efficiency of the system. The algorithm, presented in this paper, tackles the scheduling problem in a multi-layer multiprocessor environment, which exists in many computing and industrial applications. Based on the scheduling terminology, the problem can be defined as multiprocessor task scheduling in hybrid flow-shops. This paper presents a particle swarm optimization algorithm for the solution and reports its performance. The results are compared with other well known meta-heuristic techniques proposed for the solution of the same problem. Our results show that particle swarm optimization has merits in solving multiprocessor task scheduling in a hybrid flow-shop environment.
Description: International Conference on Computational Science and its Applications, ICCSA 2007, Kuala Lumpur, 26-29 August 2007
URI: http://hdl.handle.net/10397/22257
ISBN: 9783540744825
ISSN: 0302-9743
EISSN: 1611-3349
Appears in Collections:Conference Paper

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