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Population Dynamics Indicators for Evolutionary Many-Objective Optimization

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Progress in Advanced Computing and Intelligent Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 714))

Abstract

Recent research on multi- and many-objective optimization has led to the development of various state-of-the-art algorithms which produce satisfactory results for various kinds of problems. However, in real life, the underlying objective functions or the characteristic landscape formed by the objectives may not be known beforehand. This makes it difficult for a user to choose the correct optimization algorithm. This paper proposes new indicators which attempt to summarize the population dynamics across iterations. The statistics of the population movement can help in identifying various features of the problem at hand and the capacity of an algorithm to deal with the challenges corresponding to the features. The analysis of population movement can enable further modifications of an existing algorithm according to the optimization problem. The indicators can also help in the development of adaptive optimization algorithms by providing feedback during the search for optimality.

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Acknowledgements

This work is funded by the project (DST-INRIA/2015-02/BIDEE/0978) of the Indo-French Centre for the Promotion of Advanced Research (IFCPAR).

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Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Patna, Patna, India

    Raunak Sengupta

  2. Department of Computer Science and Technology, Indian Institute of Technology Patna, Patna, India

    Sriparna Saha

  3. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

    Monalisa Pal & Sanghamitra Bandyopadhyay

Authors
  1. Raunak Sengupta
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  2. Monalisa Pal
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  3. Sriparna Saha
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  4. Sanghamitra Bandyopadhyay
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Corresponding author

Correspondence to Monalisa Pal .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India

    Chhabi Rani Panigrahi

  2. Department of Computer Science, Central University of Rajasthan, Ajmer, Rajasthan, India

    Arun K. Pujari

  3. Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Sudip Misra

  4. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India

    Bibudhendu Pati

  5. Department of Computer Science and Information Engineering (CSIE), Providence University, Taichung, Taiwan

    Kuan-Ching Li

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Cite this paper

Sengupta, R., Pal, M., Saha, S., Bandyopadhyay, S. (2019). Population Dynamics Indicators for Evolutionary Many-Objective Optimization. In: Panigrahi, C., Pujari, A., Misra, S., Pati, B., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 714. Springer, Singapore. https://doi.org/10.1007/978-981-13-0224-4_24

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  • DOI: https://doi.org/10.1007/978-981-13-0224-4_24

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0223-7

  • Online ISBN: 978-981-13-0224-4

  • eBook Packages: EngineeringEngineering (R0)

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