Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-08-10
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
A study on pareto-ranking based quantum-behaved particle swarm optimization (QPSO) for multiobjective optimization problems is presented in this paper.
During the iteration, an external repository is maintained to remember the nondominated solutions, from which the global best position is chosen.
The comparison between different elitist selection strategies (preference order, sigma value, and random selection) is performed on four benchmark functions and two metrics.
The results demonstrate that QPSO with preference order has comparative performance with sigma value according to different number of objectives.
Finally, QPSO with sigma value is applied to solve multiobjective flexible job-shop scheduling problems.
American Psychological Association (APA)
Tian, Na& Zhicheng, Ji. 2015. Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1075121
Modern Language Association (MLA)
Tian, Na& Zhicheng, Ji. Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization. Mathematical Problems in Engineering No. 2015 (2015), pp.1-10.
https://search.emarefa.net/detail/BIM-1075121
American Medical Association (AMA)
Tian, Na& Zhicheng, Ji. Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1075121
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1075121