article

An improved cooperative quantum-behaved particle swarm optimization

Authors:

Yangyang Li,

Rongrong Xiang,

Licheng Jiao,

Ruochen LiuAuthors Info & Claims

Soft Computing - A Fusion of Foundations, Methodologies and Applications, Volume 16, Issue 6

Pages 1061 - 1069

https://doi.org/10.1007/s00500-012-0803-y

Published: 01 June 2012 Publication History

Abstract

Particle swarm optimization (PSO) is a population-based stochastic optimization. Its parameters are easy to control, and it operates easily. But, the particle swarm optimization is a local convergence algorithm. Quantum-behaved particle swarm optimization (QPSO) overcomes this shortcoming, and outperforms original PSO. Based on classical QPSO, cooperative quantum-behaved particle swarm optimization (CQPSO) is present. This CQPSO, a particle firstly obtaining several individuals using Monte Carlo method and these individuals cooperate between them. In the experiments, five benchmark functions and six composition functions are used to test the performance of CQPSO. The results show that CQPSO performs much better than the other improved QPSO in terms of the quality of solution and computational cost.

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An improved cooperative quantum-behaved particle swarm optimization

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cover image Soft Computing - A Fusion of Foundations, Methodologies and Applications

Soft Computing - A Fusion of Foundations, Methodologies and Applications Volume 16, Issue 6

June 2012

180 pages

ISSN:1432-7643

EISSN:1433-7479

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 June 2012

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Gong CZhou NXia SHuang S(2024)Quantum particle swarm optimization algorithm based on diversity migration strategyFuture Generation Computer Systems10.1016/j.future.2024.04.008157:C(445-458)Online publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.future.2024.04.008
You QSun JPalade VPan F(2023)Quantum-behaved particle swarm optimization with dynamic grouping searching strategyIntelligent Data Analysis10.3233/IDA-22675327:3(769-789)Online publication date: 1-Jan-2023
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Kanadath AJothi JUrolagin S(2023)Multilevel Colonoscopy Histopathology Image Segmentation Using Particle Swarm Optimization TechniquesSN Computer Science10.1007/s42979-023-01915-w4:5Online publication date: 8-Jun-2023
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Tang DZhao JYang JLiu ZCai Y(2021)An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and ApplicationsComputational Intelligence and Neuroscience10.1155/2021/89281822021Online publication date: 14-Dec-2021
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Bhatia ASaggi MZheng S(2020)QPSO-CD: quantum-behaved particle swarm optimization algorithm with Cauchy distributionQuantum Information Processing10.1007/s11128-020-02842-y19:10Online publication date: 22-Sep-2020
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Zhang YJin Z(2020)Quantum-behaved particle swarm optimization with generalized space transformation searchSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-04850-724:19(14981-14997)Online publication date: 1-Oct-2020
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Ghasemi MAkbari ERahimnejad ARazavi SGhavidel SLi L(2019)Phasor particle swarm optimization: a simple and efficient variant of PSOSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3536-823:19(9701-9718)Online publication date: 1-Oct-2019
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Djemame SBatouche MOulhadj HSiarry P(2019)Solving reverse emergence with quantum PSO application to image processingSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3331-623:16(6921-6935)Online publication date: 1-Aug-2019
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Wu AYang Z(2018)An Elitist Transposon Quantum-Based Particle Swarm Optimization Algorithm for Economic Dispatch ProblemsComplexity10.1155/2018/72765852018Online publication date: 17-Jul-2018
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A new quantum behaved particle swarm optimization

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