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An Improved Cohort Intelligence with Panoptic Learning Behavior for Solving Constrained Problems

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Constraint Handling in Metaheuristics and Applications

Abstract

In this paper, we present a new optimization algorithm referred to as Cohort Intelligence with Panoptic learning (CI-PL). This proposed algorithm is a modified version of Cohort Intelligence (CI), where Panoptic learning (PL) is incorporated into CI which makes every cohort candidate learn the most from the best candidate but at same time it does not completely ignore the other candidates. The PL is assisted with a new sampling interval reduction method based on the standard deviation between the behaviors of the cohort candidates. A variety of well-known set of unconstrained and constrained test problems have been successfully solved by using the proposed algorithm. The CI-PL approach produced competent and sufficiently robust results solving unconstrained, constrained, and engineering problems. The associated strengths, weaknesses, and possible real-world extensions are also discussed.

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Author information

Authors and Affiliations

  1. Monash University Malaysia, Jalan Lagoon Selatan Bandar Sunway, 47500, Subang Jaya, Selangor, Malaysia

    Ganesh Krishnasamy

  2. Symbiosis Center for Research and Innovation, Symbiosis International (Deemed University), Pune, 412115, Maharashtra, India

    Anand J. Kulkarni

  3. Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, 412115, Maharashtra, India

    Apoorva S. Shastri

Authors
  1. Ganesh Krishnasamy
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  2. Anand J. Kulkarni
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  3. Apoorva S. Shastri
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Corresponding author

Correspondence to Anand J. Kulkarni .

Editor information

Editors and Affiliations

  1. Symbiosis Centre for Research and Innovation, Symbiosis International University, Pune, Maharashtra, India

    Anand J. Kulkarni

  2. Artificial Intelligence Research Center, University of Veracruz, Xalapa, Veracruz, Mexico

    Efrén Mezura-Montes

  3. School of Automation, Central South University, Changsha, China

    Yong Wang

  4. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia

    Amir H. Gandomi

  5. Monash University Malaysia, Selangor, Malaysia

    Ganesh Krishnasamy

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Krishnasamy, G., Kulkarni, A.J., Shastri, A.S. (2021). An Improved Cohort Intelligence with Panoptic Learning Behavior for Solving Constrained Problems. In: Kulkarni, A.J., Mezura-Montes, E., Wang, Y., Gandomi, A.H., Krishnasamy, G. (eds) Constraint Handling in Metaheuristics and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-33-6710-4_2

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  • DOI: https://doi.org/10.1007/978-981-33-6710-4_2

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