Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11643
Title: A particle swarm optimization-based approach to tackling simulation optimization of stochastic, large-scale and complex systems
Authors: Lu, M
Wu, DP
Zhang, JP
Issue Date: 2006
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2006, v. 3930 lnai, p. 528-537 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: In this research, the methodology of particle swarm optimization (PSO) combined with discrete system simulation is described and employed for enhancing logistical and operational efficiencies of practical one-plant-multi-site concrete delivery systems. In a case study using data from a concrete plant in Hong Kong, PSO was compared with the genetic algorithms (GA) in assessing two mechanisms for optimizing stochastic simulation systems, namely, "steady, averaging" and "non-steady, stochastic". The results show our PSO-based approach could rapidly (5 minutes) converge at the minimum level for an output of the simulation model while GA failed to converge or required a long time (about 1.5 hours) in search of the minimum. In conclusion, the proposed optimization procedures hold the potential to provide a generic, efficient approach to tackling simulation optimization of stochastic, large-scale and complex systems.
Description: 4th International Conference on Machine Learning and Cybernetics, ICMLC 2005, Guangzhou, 18-21 August 2005
URI: http://hdl.handle.net/10397/11643
ISBN: 3540335846
9783540335849
ISSN: 0302-9743
EISSN: 1611-3349
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