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Integral-Controlled Particle Swarm Optimization

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Handbook of Swarm Intelligence

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 8))

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Summary

Particle swarm optimization (PSO) is a novel population-based stochastic optimization algorithm. However, it gets easily trapped into local optima when dealing with multi-modal high-dimensional problems. To overcome this shortcoming, two integral controllers are incorporated into the methodology of PSO, and the integral-controlled particle swarm optimization (ICPSO) is introduced. Due to the additional accelerator items, the behavior of ICPSO is more complex, and provides more chances to escaping from a local optimum than the standard version of PSO. However, many experimental results show the performance of ICPSO is not always well because of the particles’ un-controlled movements. Therefore, a new variant, integral particle swarm optimization with dispersed accelerator information (IPSO-DAI) is designed to improve the computational efficiency. In IPSO-DAI, a predefined predicted velocity index is introduced to guide the moving direction. If the average velocity of one particle is superior to the index value, it will choice a convergent manner, otherwise, a divergent manner is employed. Furthermore, the choice of convergent manner or divergent manner for each particle is associated with its performance to fit different living experiences. Simulation results show IPSO-DAI is more effective than other three variants of PSO especially for multi-modal numerical problems. The IPSO-DAI is also applied to directing the orbits of discrete chaotic dynamical systems by adding small bounded perturbations, and achieves the best performance among four different variants of PSO.

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Author information

Authors and Affiliations

  1. Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, No.66 Waliu Road, Wanbailin District, Taiyuan, Shanxi, China, 030024

    Zhihua Cui, Xingjuan Cai, Ying Tan & Jianchao Zeng

Authors
  1. Zhihua Cui
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  2. Xingjuan Cai
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  3. Ying Tan
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  4. Jianchao Zeng
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Editor information

Editors and Affiliations

  1. Electrical Engineering Department, Indian Institute of Technology, Delhi , 110016, New Delhi, India

    Bijaya Ketan Panigrahi

  2. Director of Research and Postgraduate Office, Xi’an Jiaotong-Liverpool University , 215123, Suzhou, China

    Yuhui Shi

  3. School of Electrical & Electronic Engineering, Nanyang Technological University , 639798, Singapore

    Meng-Hiot Lim

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© 2011 Springer-Verlag Berlin Heidelberg

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Cui, Z., Cai, X., Tan, Y., Zeng, J. (2011). Integral-Controlled Particle Swarm Optimization. In: Panigrahi, B.K., Shi, Y., Lim, MH. (eds) Handbook of Swarm Intelligence. Adaptation, Learning, and Optimization, vol 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17390-5_8

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  • DOI: https://doi.org/10.1007/978-3-642-17390-5_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17389-9

  • Online ISBN: 978-3-642-17390-5

  • eBook Packages: EngineeringEngineering (R0)

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