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Particle Swarm Optimization: Performance Tuning and Empirical Analysis

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Foundations of Computational Intelligence Volume 3

Part of the book series: Studies in Computational Intelligence ((SCI,volume 203))

Abstract

This chapter presents some of the recent modified variants of Particle Swarm Optimization (PSO). The main focus is on the design and implementation of the modified PSO based on diversity, Mutation, Crossover and efficient Initialization using different distributions and Low-discrepancy sequences. These algorithms are applied to various benchmark problems including unimodal, multimodal, noisy functions and real life applications in engineering fields. The effectiveness of the algorithms is discussed.

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Author information

Authors and Affiliations

  1. Department of Paper Technology, IIT Roorkee, India

    Millie Pant & Radha Thangaraj

  2. Q2S, Norwegian University of Science and Technology, Norway

    Ajith Abraham

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  1. Millie Pant
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  2. Radha Thangaraj
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  3. Ajith Abraham
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Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Auburn, Washington, USA

    Ajith Abraham

  2. College of Business Administration Quantitative and Information System Department, Kuwait University, P.O. Box 5486, 13055, Safat, Kuwait

    Aboul-Ella Hassanien

  3. Université Paris XII Fac. Sciences, LERISS, 61 avenue du Général de Gaulle Building P2 - Room 350, 94010, Créteil, France

    Patrick Siarry

  4. Department of Computer Science, University of Pretoria, 0002, Pretoria, South Africa

    Andries Engelbrecht

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Pant, M., Thangaraj, R., Abraham, A. (2009). Particle Swarm Optimization: Performance Tuning and Empirical Analysis. In: Abraham, A., Hassanien, AE., Siarry, P., Engelbrecht, A. (eds) Foundations of Computational Intelligence Volume 3. Studies in Computational Intelligence, vol 203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01085-9_5

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  • DOI: https://doi.org/10.1007/978-3-642-01085-9_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01084-2

  • Online ISBN: 978-3-642-01085-9

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