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Soft Computing
Published in: Soft Computing 11/2014

01-11-2014 | Methodologies and Application

A revised discrete particle swarm optimization algorithm for permutation flow-shop scheduling problem

Authors: Chun-Lung Chen, Shin-Ying Huang, Yeu-Ruey Tzeng, Chuen-Lung Chen

Published in: Soft Computing | Issue 11/2014

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Abstract

This research proposes a revised discrete particle swarm optimization (RDPSO) to solve the permutation flow-shop scheduling problem with the objective of minimizing makespan (PFSP-makespan). The candidate problem is one of the most studied NP-complete scheduling problems. RDPSO proposes new particle swarm learning strategies to thoroughly study how to properly apply the global best solution and the personal best solution to guide the search of RDPSO. A new filtered local search is developed to filter the solution regions that have been reviewed and guide the search to new solution regions in order to keep the search from premature convergence. Computational experiments on Taillard’s benchmark problem sets demonstrate that RDPSO significantly outperforms all the existing PSO algorithms.
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Metadata
Title
A revised discrete particle swarm optimization algorithm for permutation flow-shop scheduling problem
Authors
Chun-Lung Chen
Shin-Ying Huang
Yeu-Ruey Tzeng
Chuen-Lung Chen
Publication date
01-11-2014
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 11/2014
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-013-1199-z

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