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An Analysis of Nonlinear Acceleration Coefficients Adjustment for PSO

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Artificial Intelligence and Computational Intelligence (AICI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7530))

Abstract

Linear acceleration coefficients adjustment had been widely used in particle swarm optimization (PSO). In this paper, a novel nonlinear strategy is developed, where the acceleration coefficients including both cognitive component and social component are adjusted nonlinearly to improve the optimization performance within a reasonable iteration times. Furthermore, the novel adjustment is deeply analyzed by experimental simulations based on four standard test functions. The results confirm the validity of the nonlinear parameter adjustment method in terms of the balance between convergence rate and optimization accuracy.

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Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, China

    Gang Wang & Zhikun Liu

Authors
  1. Gang Wang
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  2. Zhikun Liu
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Editor information

Editors and Affiliations

  1. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

  2. Deaprtment of Business Administration, Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan O, Hong Kong, China

    Fu Lee Wang

  3. School of Business Information Technology and Logistics, RMIT University, City Campus, 124 la Tobre Street, 3000, Melbourne, Victoria, Australia

    Hepu Deng

  4. School of Electronics and Information, Tongji University, 201804, Shanghai, China

    Duoqian Miao

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© 2012 Springer-Verlag Berlin Heidelberg

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Wang, G., Liu, Z. (2012). An Analysis of Nonlinear Acceleration Coefficients Adjustment for PSO. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Artificial Intelligence and Computational Intelligence. AICI 2012. Lecture Notes in Computer Science(), vol 7530. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33478-8_86

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  • DOI: https://doi.org/10.1007/978-3-642-33478-8_86

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33477-1

  • Online ISBN: 978-3-642-33478-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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