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Enhanced Asynchronous Differential Evolution Using Trigonometric Mutation

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Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016) (SoCPaR 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 614))

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Abstract

The Asynchronous Differential Evolution (ADE) is based on Differential Evolution (DE) with some variations. In ADE the population is updated as soon as a vector with better fitness is found hence the algorithm works asynchronously. ADE leads to stronger exploration and supports parallel optimization. In this paper ADE is embedded with the trigonometric mutation operator (TMO) to enhance the convergence rate of basic ADE. The proposed hybridized algorithm is termed as ADE-TMO. The algorithm is verified over widely used 10 benchmark functions referred from the literature. The simulated results show that ADE-TMO perform better than basic ADE and other state-of-art algorithms.

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Author information

Authors and Affiliations

  1. Amity University Rajasthan, Jaipur, India

    Vaishali, Tarun K. Sharma & Jitendra Rajpurohit

  2. Machine Intelligence Research Lab, Stillwater, USA

    Ajith Abraham

Authors
  1. Vaishali
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  2. Tarun K. Sharma
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  3. Ajith Abraham
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  4. Jitendra Rajpurohit
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Corresponding author

Correspondence to Vaishali .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research, Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA

    Ajith Abraham

  2. VIT University, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. School of Engineering, Polytechnic of Porto (ISEP/IPP), Porto, Portugal

    Ana Maria Madureira

  4. Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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Vaishali, Sharma, T.K., Abraham, A., Rajpurohit, J. (2018). Enhanced Asynchronous Differential Evolution Using Trigonometric Mutation. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_38

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  • DOI: https://doi.org/10.1007/978-3-319-60618-7_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60617-0

  • Online ISBN: 978-3-319-60618-7

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