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Multimodal Function Optimization Using an Improved Bat Algorithm in Noise-Free and Noisy Environments

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Nature-Inspired Computing and Optimization

Part of the book series: Modeling and Optimization in Science and Technologies ((MOST,volume 10))

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Abstract

Modern optimization problems in economics, medicine, and engineering are becoming more complicated and have a convoluted search space with multiple minima. These problems are multimodal with objective functions exhibiting multiple peaks, valleys, and hyperplanes of varying heights. Furthermore, they are nonlinear, non-smooth, non-quadratic, and can have multiple satisfactory solutions. In order to select a best solution among several possible solutions that can meet the problem objectives, it is desirable to find many such solutions. For these problems, the gradient information is either not available or not computable within reasonable time. Therefore, solving such problems is a challenging task. Recent years have seen a plethora of activities to solve such multimodal problems using non-traditional methods. These methods are nature inspired and are becoming popular due to their general applicability and effective search strategies. In this chapter, we assess the ability of an improved bat algorithm (IBA) to solve multimodal problems in noise-free and additive white Gaussian noise (AWGN) environments. Numerical results are presented to show that the IBA can successfully locate multiple solutions in both noise-free and AWGN environments with a relatively high degree of accuracy.

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Author information

Authors and Affiliations

  1. Harman/Becker Automotive Systems GmbH, Becker-Goering Str. 16, 76307, Karlsbad, Germany

    Momin Jamil

  2. Blekinge Institute of Technology, 371 79, Karlskrona, Sweden

    Hans-Jürgen Zepernick

  3. Middlesex University, School of Science and Technology, London, NW4 4BT, UK

    Xin-She Yang

Authors
  1. Momin Jamil
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  2. Hans-Jürgen Zepernick
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  3. Xin-She Yang
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Corresponding author

Correspondence to Momin Jamil .

Editor information

Editors and Affiliations

  1. Dept of Computer Science and Engineering, SOA University Dept of Computer Science and Engineering, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  2. School of Science and Technology, Middlesex University School of Science and Technology, London, United Kingdom

    Xin-She Yang

  3. School of H.S.E., University of Hyogo School of H.S.E., Himeji, Japan

    Kazumi Nakamatsu

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Jamil, M., Zepernick, HJ., Yang, XS. (2017). Multimodal Function Optimization Using an Improved Bat Algorithm in Noise-Free and Noisy Environments. In: Patnaik, S., Yang, XS., Nakamatsu, K. (eds) Nature-Inspired Computing and Optimization. Modeling and Optimization in Science and Technologies, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-50920-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-50920-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50919-8

  • Online ISBN: 978-3-319-50920-4

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