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A Novel Restart Strategy for Solving Complex Multi-modal Optimization Problems Using Real-Coded Genetic Algorithm

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Book cover Intelligent Systems Design and Applications (ISDA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 736))

Abstract

Genetic algorithm (GA) is one of the most popular and robust stochastic optimization tools used in various fields of research and industrial applications. It had been applied for solving many global optimization problems for the last few decades. However, it has a poor theoretical assurance to reach the globally optimal solutions, while solving the complex multi-modal problems. Restart strategy plays an important role in overcoming this limitation of a GA to a certain extent. Although there are a few restart methods available in the literature, these are not adequate. In this paper, a novel restart strategy is proposed for solving complex multi-modal optimization problems using a real-coded genetic algorithm (RCGA). To show the superiority of the proposed scheme, ten complex multi-modal test functions have been selected from the CEC 2005 benchmark functions and its results are compared with that of the other strategies.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India

    Amit Kumar Das & Dilip Kumar Pratihar

Authors
  1. Amit Kumar Das
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  2. Dilip Kumar Pratihar
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Correspondence to Dilip Kumar Pratihar .

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Editors and Affiliations

  1. Machine Intelligence Research Labs , Auburn, Washington, USA

    Ajith Abraham

  2. Department of Computer Science, South Asian University, Chanakyapuri, Delhi, India

    Pranab Kr. Muhuri

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka , Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  4. Machine Intelligence Research Labs , Auburn, Washington, USA

    Niketa Gandhi

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Das, A.K., Pratihar, D.K. (2018). A Novel Restart Strategy for Solving Complex Multi-modal Optimization Problems Using Real-Coded Genetic Algorithm. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-76348-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76347-7

  • Online ISBN: 978-3-319-76348-4

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