Paper Title:
Application of Quantum-Behaved Particle Swarm Optimization in Engineering Constrained Optimization Problems
  Abstract

To overcome the shortage of standard Particle Swarm Optimization(SPSO) on premature convergence, Quantum-behaved Particle Swarm Optimization (QPSO) is presented to solve engineering constrained optimization problem. QPSO algorithm is a novel PSO algorithm model in terms of quantum mechanics. The model is based on Delta potential, and we think the particle has the behavior of quanta. Because the particle doesn’t have a certain trajectory, it has more randomicity than the particle which has fixed path in PSO, thus the QPSO more easily escapes from local optima, and has more capability to seek the global optimal solution. In the period of iterative optimization, outside point method is used to deal with those particles that violate the constraints. Furthermore, compared with other intelligent algorithms, the QPSO is verified by two instances of engineering constrained optimization, experimental results indicate that the algorithm performs better in terms of accuracy and robustness.

  Info
Periodical
Advanced Materials Research (Volumes 383-390)
Chapter
Chapter 29: Nanofabrication, Nanometrology and Applications
Edited by
Wu Fan
Pages
7208-7213
DOI
10.4028/www.scientific.net/AMR.383-390.7208
Citation
D. K. Tan, "Application of Quantum-Behaved Particle Swarm Optimization in Engineering Constrained Optimization Problems", Advanced Materials Research, Vols. 383-390, pp. 7208-7213, 2012
Online since
November 2011
Authors
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Price
$32.00
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