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An Adaptive Parameter Free Particle Swarm Optimization Algorithm for the Permutation Flowshop Scheduling Problem

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Machine Learning, Optimization, and Data Science (LOD 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11943))

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Abstract

The finding of suitable values for all parameters of a Particle Swarm Optimization (PSO) algorithm is a crucial issue in the design of the algorithm. A trial and error procedure is the most common way to find the parameters but, also, a number of different procedures have been applied in the past. In this paper, an adaptive strategy is used where random values are assigned in the initialization of the algorithm and, then, during the iterations the parameters are optimized together and simultaneously with the optimization of the objective function of the problem. This approach is used for the solution of the Permutation Flowshop Scheduling Problem. The algorithm is tested in 120 benchmark instances and is compared with a number of algorithms from the literature.

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Authors and Affiliations

  1. School of Production Engineering and Management, Technical University of Crete, Chania, Greece

    Yannis Marinakis & Magdalene Marinaki

Authors
  1. Yannis Marinakis
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  2. Magdalene Marinaki
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Corresponding author

Correspondence to Yannis Marinakis .

Editor information

Editors and Affiliations

  1. University of Cambridge, Cambridge, UK

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. Harvard University, Cambridge, MA, USA

    Renato Umeton

  4. Università di Catania, Catania, Catania, Italy

    Giovanni Giuffrida

  5. Almawave, Rome, Roma, Italy

    Vincenzo Sciacca

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Marinakis, Y., Marinaki, M. (2019). An Adaptive Parameter Free Particle Swarm Optimization Algorithm for the Permutation Flowshop Scheduling Problem. In: Nicosia, G., Pardalos, P., Umeton, R., Giuffrida, G., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2019. Lecture Notes in Computer Science(), vol 11943. Springer, Cham. https://doi.org/10.1007/978-3-030-37599-7_15

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  • DOI: https://doi.org/10.1007/978-3-030-37599-7_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37598-0

  • Online ISBN: 978-3-030-37599-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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