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Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization (2015)

Na Tian and Zhicheng Ji

Hindawi

In: Mathematical Problems in Engineering

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Publication Date: 2015-08-11

Description: 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.

Print ISSN: 1024-123X

Electronic ISSN: 1563-5147

Topics: Mathematics , Technology

Published by Hindawi

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