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Trigonometric Probability Tuning in Asynchronous Differential Evolution

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Soft Computing: Theories and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 584))

Abstract

Asynchronous differential evolution (ADE) has been derived from 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 incorporated with the trigonometric mutation operator to enhance the convergence rate, and the performance of the algorithm is tested for various values of trigonometric mutation probability; that is, the tuning of trigonometric mutation probability has been done to obtain its optimum setting. The proposed work is termed as ADE–trigonometric probability tuning (ADE-TPT). For tuning, the tests have been done over widely used benchmark functions referred from the literature, and the results obtained using different probabilities are compared.

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Author information

Authors and Affiliations

  1. Amity University Rajasthan, Jaipur, 303007, India

    Vaishali, Tarun Kumar Sharma & Jitendra Rajpurohit

  2. Machine Intelligence Research Lab, Auburn, WA, USA

    Ajith Abraham

Authors
  1. Vaishali
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  2. Tarun Kumar 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. Department of Applied Science and Engineering, IIT Roorkee, Saharanpur, India

    Millie Pant

  2. Department of Physics, Amity School of Applied Sciences, Amity University Rajasthan, Jaipur, Rajasthan, India

    Kanad Ray

  3. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, Rajasthan, India

    Tarun K. Sharma

  4. Department of Electronics and Communication Engineering, SEEC, Manipal University Jaipur, Jaipur, Rajasthan, India

    Sanyog Rawat

  5. Surface Characterization Group, NIMS, Nano Characterization Unit, Advanced Key Technologies Division, Tsukuba, Ibaraki, Japan

    Anirban Bandyopadhyay

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Vaishali, Sharma, T.K., Abraham, A., Rajpurohit, J. (2018). Trigonometric Probability Tuning in Asynchronous Differential Evolution. In: Pant, M., Ray, K., Sharma, T., Rawat, S., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 584. Springer, Singapore. https://doi.org/10.1007/978-981-10-5699-4_26

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  • DOI: https://doi.org/10.1007/978-981-10-5699-4_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5698-7

  • Online ISBN: 978-981-10-5699-4

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