An Improved Algorithm of Quantum Particle Swarm Optimization

Based on the classical particle optimization algorithm and the quantum behavioral theory, this article proposes an improved QPSO algorithm -- GLQPSO to perfect the global and local convergence speed ability and speed of classical particle swarm. To achieve this purpose, the author introduces an impr...

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Bibliographic Details
Published in:Journal of software 2014-11, Vol.9 (11), p.2789-2789
Main Authors: Jin, Yan-xia, Xue, Jing, Shi, Zhi-bin
Format: Article
Language:English
Subjects:
Algorithms
Chaos theory
Computer programs
Convergence
Logistics
Optimization
Software
Swarm intelligence
Citations: Items that this one cites
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Summary:Based on the classical particle optimization algorithm and the quantum behavioral theory, this article proposes an improved QPSO algorithm -- GLQPSO to perfect the global and local convergence speed ability and speed of classical particle swarm. To achieve this purpose, the author introduces an improved Logistic chaotic mapping theory 1 to conduct chaotic search for the initial particle and chaotic remolding of the locally optimized particle swarm. The test of the classical function has proved the success of this effort.
ISSN:1796-217X
1796-217X
DOI:10.4304/jsw.9.11.2789-2795