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Headless Chicken Particle Swarm Optimization Algorithms

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Advances in Swarm Intelligence (ICSI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9712))

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Abstract

This paper investigates various strategies for implementing the headless chicken macromutation operator in the particle swarm optimization domain. Three different headless chicken particle swarm optimization algorithms are proposed and evaluated against a standard guaranteed convergence PSO algorithm on a diverse set of benchmark problems. Competitive performance is demonstrated by a Von Neumann headless chicken particle swarm optimization algorithm when compared to a classic guaranteed convergence particle swarm optimization algorithm. Statistically significantly superior results are obtained over a number of difficult benchmark problems.

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Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Pretoria, Pretoria, South Africa

    Jacomine Grobler

  2. Department of Computer Science, University of Pretoria, Pretoria, South Africa

    Andries P. Engelbrecht

Authors
  1. Jacomine Grobler
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  2. Andries P. Engelbrecht
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Corresponding author

Correspondence to Jacomine Grobler .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

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© 2016 Springer International Publishing Switzerland

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Grobler, J., Engelbrecht, A.P. (2016). Headless Chicken Particle Swarm Optimization Algorithms. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_35

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  • DOI: https://doi.org/10.1007/978-3-319-41000-5_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40999-3

  • Online ISBN: 978-3-319-41000-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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