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Magnifier Particle Swarm Optimization

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Book cover Nature-Inspired Algorithms for Optimisation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 193))

Abstract

In this chapter, after a brief introduction to the Particle Swarm Optimization (PSO), a novel PSO algorithm based on magnification transformation called Magnifier Particle Swarm Optimization (MPSO) is presented. In the MPSO, the range around each generation’s best individual is enlarged akin to using a magnifier, while the velocity of particles stays unchanged. This way, the MPSO achieves much faster convergence speed and better optimization solving capability than the Standard Particle Swarm Optimization (SPSO) by a number of simulations. In the context, the proposed MPSO algorithm is described and explained in detail by comparing it with the SPSO. Simulations on thirteen benchmark test functions are conducted to verify the effectiveness of the MPSO. Our experimental results show that the proposed MPSO not only speeds up the convergence tremendously but also maintains a strong capability of searching for the global solution with high accuracy. The application to spam detection shows that the proposed MPSO gives a promising result.

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Author information

Authors and Affiliations

  1. Key Laboratory of Machine Perception (Ministry of Education), Peking University,  

    Ying Tan & Junqi Zhang

  2. Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, P.R. China

    Ying Tan & Junqi Zhang

Authors
  1. Ying Tan
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  2. Junqi Zhang
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Editor information

Editors and Affiliations

  1. Swinburne University of Technology, Sarawak Campus, Jalan Simpang Tiga, 93350, Kuching, Sarawak, Malaysia

    Raymond Chiong

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Tan, Y., Zhang, J. (2009). Magnifier Particle Swarm Optimization. In: Chiong, R. (eds) Nature-Inspired Algorithms for Optimisation. Studies in Computational Intelligence, vol 193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00267-0_10

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  • DOI: https://doi.org/10.1007/978-3-642-00267-0_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00266-3

  • Online ISBN: 978-3-642-00267-0

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