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Particle Swarm Optimization with Opposite Particles

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MICAI 2005: Advances in Artificial Intelligence (MICAI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3789))

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Abstract

The particle swarm optimization algorithm is a kind of intelligent optimization algorithm. This algorithm is prone to be fettered by the local optimization solution when the particle’s velocity is small. This paper presents a novel particle swarm optimization algorithm named particle swarm optimization with opposite particles which is guaranteed to converge to the global optimization solution with probability one. And we also make the global convergence analysis. Finally, three function optimizations are simulated to show that the PSOOP is better and more efficient than the PSO with inertia weights.

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Authors and Affiliations

  1. Institute of Intelligent Machines (IIM) of Chinese Academy of Science, 230031, Hefei, Anhui, P.R. China

    Rujing Wang & Xiaoming Zhang

Authors
  1. Rujing Wang
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  2. Xiaoming Zhang
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Editor information

Editors and Affiliations

  1. National Polytechnic Institute, Center for Computing Research, 07738, Mexico City, México

    Alexander Gelbukh

  2. Technológico de Monterrey (ITESM), Campus Ciudad de México (CCM), Calle del Puente 222, Col. Ejudos de Huipulco, 14360 DF, Tlalpan, Mexico

    Álvaro de Albornoz

  3. Center for Intelligent Systems, Tecnológico de Monterrey, Campus Monterrey, 64849, Monterrey, N.L., Mexico

    Hugo Terashima-Marín

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© 2005 Springer-Verlag Berlin Heidelberg

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Wang, R., Zhang, X. (2005). Particle Swarm Optimization with Opposite Particles. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_64

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  • DOI: https://doi.org/10.1007/11579427_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29896-0

  • Online ISBN: 978-3-540-31653-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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