Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27212
Title: Efficient optimization procedures for stochastic simulation systems
Authors: Wu, D
Lu, M
Zhang, J
Keywords: Civil engineering computing
Concrete
Genetic algorithms
Particle swarm optimisation
Simulation
Stochastic processes
Issue Date: 2005
Publisher: IEEE
Source: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, 2005, 18-21 August 2005, Guangzhou, China, v. 5, p. 2895-2900 How to cite?
Abstract: In the research presented, we applied the particle swarm optimization (PSO) technique to optimize a concrete delivery operations simulation model (named HKCONSIM), aimed at improving the overall operational efficiency by minimizing the nonproductive time incurred on multiple building sites. Along with the conventional "steady, averaging" simulation-optimization mechanism, we proposed, assessed a "non-steady, stochastic" optimization mechanism, and further compared PSO with GA in applying the two mechanisms on a case study. It was found PSO was able to rapidly find the optimum on an output of a stochastic simulation model in the "non-steady, stochastic" setting, while GA failed to converge. Compared with the performance of GA on the conventional "steady, averaging" optimization setting, the use of PSO in the "non-steady, stochastic" setting resulted in a marked improvement in light of the optimum-seeking time, requiring about 5 minutes PC time as opposed to about 1.5 hours taken by GA. Therefore, the proposed PSO-based, "non-steady, stochastic" optimization procedures can comfortably, rapidly approach the optimum state for a large-scale, complex system simulation of realistic granularity.
URI: http://hdl.handle.net/10397/27212
ISBN: 0-7803-9091-1
DOI: 10.1109/ICMLC.2005.1527437
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

35
Last Week
2
Last month
Checked on Sep 17, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.