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Parameter Optimization Methods Based on Computational Intelligence Techniques in Context of Sustainable Computing

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Intelligent Decision Support Systems for Sustainable Computing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 705))

Abstract

In sustainable computing techniques, we always need to solve lots of optimization problems like design, planning and control, which are extremely hard. Conventional mathematical optimization techniques are computationally difficult. Recent advances in computational intelligence have resulted in an increasing number of nature inspired metaheuristic optimization techniques for effectively solve these complex problems. Mainly, the algorithms which are based on the principle of natural biological evolution and/or collective behavior of swarm have shown a promising performance and are becoming more and more popular nowadays. Most of these algorithms have their some set of parameters. The performance of these algorithm is highly depends on optimal parameter value settings. Prior to running these algorithms, the user must have values of different parameters, such as population size, parameters related to selection, and crossover probability, number of generations etc. That is energy and resource consuming. In this paper we summarize the work in computational intelligence based parameter setting techniques, and discuss related methodological issues. Further we discuss how parameter tuning affects the performance and/or robustness of metaheuristic algorithms and also discusses parameter tuning taxonomy.

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Authors and Affiliations

  1. Computer Engineering Department, NIT Kurukshetra, Kurukshetra, Haryana, India

    Pankaj Upadhyay & Jitender Kumar Chhabra

Authors
  1. Pankaj Upadhyay
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  2. Jitender Kumar Chhabra
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Correspondence to Pankaj Upadhyay .

Editor information

Editors and Affiliations

  1. VIT University , Vellore, Tamil Nadu, India

    Arun Kumar Sangaiah

  2. Sci N/w for Innova and Research Exc, Machine Intelligence Research Labs Sci N/w for Innova and Research Exc, Auburn, Washington, USA

    Ajith Abraham

  3. Faculté des Sciences et Technologie, Université Paris-Est Créteil Val-de-Marn Faculté des Sciences et Technologie, Créteil Cedex, France

    Patrick Siarry

  4. Faculty of Engig Computer & Math sci, The University of Adelaide Faculty of Engig Computer & Math sci, Adelaide, South Australia, Australia

    Michael Sheng

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Upadhyay, P., Chhabra, J.K. (2017). Parameter Optimization Methods Based on Computational Intelligence Techniques in Context of Sustainable Computing. In: Sangaiah, A., Abraham, A., Siarry, P., Sheng, M. (eds) Intelligent Decision Support Systems for Sustainable Computing. Studies in Computational Intelligence, vol 705. Springer, Cham. https://doi.org/10.1007/978-3-319-53153-3_6

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  • DOI: https://doi.org/10.1007/978-3-319-53153-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53152-6

  • Online ISBN: 978-3-319-53153-3

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