A hybrid improved quantum-behaved particle swarm optimization algorithm using adaptive coefficients and natural selection method

To improve the precision and convergence performance of the QPSO, this paper present a hybrid improved QPSO algorithm, called LTQPSO, by combining QPSO with the individual particle evolutionary rate, swarm dispersion and natural selection method. In LTQPSO, the individual particle evolutionary rate...

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Bibliographic Details
Published in2015 Seventh International Conference on Advanced Computational Intelligence (ICACI) pp. 312 - 317
Main Authors Qin Qian, Myongchol Tokgo, Cholwon Kim, Cholhun Han, Junchol Ri, Kumsong Song
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2015
Subjects
Aircraft
Benchmark testing
Convergence
Technological innovation
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Summary:To improve the precision and convergence performance of the QPSO, this paper present a hybrid improved QPSO algorithm, called LTQPSO, by combining QPSO with the individual particle evolutionary rate, swarm dispersion and natural selection method. In LTQPSO, the individual particle evolutionary rate and swarm dispersion are used to approximate the objective function around a current position with high quality in the search space. Natural selection method is used to update from the worst position to best position in the swarm. Experimental results on several well-known benchmark functions demonstrate that the proposed LTQPSO performs much better than QPSO and other variants of QPSO in terms of their convergence and stability.
ISBN:1479972576
9781479972579
DOI:10.1109/ICACI.2015.7184720