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Differential Evolution Via Exploiting Opposite Populations

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Oppositional Concepts in Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 155))

Summary

The concept of opposition can contribute to improve the performance of population-based algorithms. This chapter presents an overview of a novel opposition-based scheme to accelerate an evolutionary algorithm, differential evolution (DE). The proposed opposition-based DE (ODE) employs opposition-based computation (OBC) for population initialization and also for generation jumping. Opposite numbers, representing anti-chromosomes, have been utilized to improve the convergence rate of the classical DE. A test suite with 15 well-known benchmark functions is employed for experimental verification. Descriptions for the DE and ODE algorithms, and a comparison strategy are provided. Results are promising and confirm that the ODE outperforms its parent algorithm DE. This work can be regarded as an initial study to exploit oppositional concepts to expedite the optimization process for any population-based approach.

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Author information

Authors and Affiliations

  1. Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), Canada

    Shahryar Rahnamayan

  2. Department of Systems Design Engineering, University of Waterloo, Canada

    H. R. Tizhoosh

Authors
  1. Shahryar Rahnamayan
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  2. H. R. Tizhoosh
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Hamid R. Tizhoosh Mario Ventresca

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Rahnamayan, S., Tizhoosh, H.R. (2008). Differential Evolution Via Exploiting Opposite Populations. In: Tizhoosh, H.R., Ventresca, M. (eds) Oppositional Concepts in Computational Intelligence. Studies in Computational Intelligence, vol 155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70829-2_8

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  • DOI: https://doi.org/10.1007/978-3-540-70829-2_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70826-1

  • Online ISBN: 978-3-540-70829-2

  • eBook Packages: EngineeringEngineering (R0)

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