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Reinforcement Learning-Based Adaptive Operator Selection

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Optimization and Learning (OLA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1443))

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Abstract

Metaheuristic and swarm intelligence approaches require devising optimisation algorithms with operators to let produce neighbouring solutions to conduct a move. The efficiency of algorithms using single operator remains recessive in comparison with those with multiple operators. However, use of multiple operators require a selection mechanism, which may not be always as productive as expected; therefore an adaptive selection scheme is always needed. In this study, an experience-based, reinforcement learning algorithm has been used to build an adaptive selection scheme implemented to work with a binary artificial bee colony algorithm in which the selection mechanism learns when and subject to which circumstances an operator can help produce better and worse neighbours. The implementations have been tested with commonly used benchmarks of uncapacitated facility location problem. The results demonstrates that the selection scheme developed based on reinforcement learning, which can also be named as smart selection scheme, performs much better that state-of-art adaptive selection schemes.

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Author information

Authors and Affiliations

  1. Engineering Faculty, Computer Engineering Department, Karabuk University, Karabük, Turkey

    Rafet Durgut

  2. UWE Bristol, Department of Computer Science and Creative Technologies, Bristol, UK

    Mehmet Emin Aydin

Authors
  1. Rafet Durgut
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  2. Mehmet Emin Aydin
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Correspondence to Rafet Durgut .

Editor information

Editors and Affiliations

  1. University of Cádiz, Cádiz, Spain

    Bernabé Dorronsoro

  2. University of Technology of Troyes, Troyes, France

    Lionel Amodeo

  3. University of Catania, Catania, Italy

    Mario Pavone

  4. University of Cádiz, Cádiz, Spain

    Patricia Ruiz

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Durgut, R., Aydin, M.E. (2021). Reinforcement Learning-Based Adaptive Operator Selection. In: Dorronsoro, B., Amodeo, L., Pavone, M., Ruiz, P. (eds) Optimization and Learning. OLA 2021. Communications in Computer and Information Science, vol 1443. Springer, Cham. https://doi.org/10.1007/978-3-030-85672-4_3

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  • DOI: https://doi.org/10.1007/978-3-030-85672-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85671-7

  • Online ISBN: 978-3-030-85672-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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