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A Hybrid Imperialist Competitive Algorithm for the Flexible Job Shop Problem

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Artificial Life and Computational Intelligence (ACALCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9592))

Abstract

Flexible job shop scheduling problem (FJSP) is one of the hardest combinatorial optimization problems known to be NP-hard. This paper proposes a novel hybrid imperialist competitive algorithm with simulated annealing (HICASA) for solving the FJSP. HICASA explores the search space by using imperial competitive algorithm (ICA) and use a simulated annealing (SA) algorithm for exploitation in the search space. In order to obtain reliable results from HICASA algorithm, a robust parameter design is applied. HICASA is compared with the widely-used genetic algorithm (GA) and the relatively new imperialist competitive algorithm (ICA). Experimental results suggest that HICASA algorithm is superior to GA and ICA on the FJSP.

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Author information

Authors and Affiliations

  1. School of Computer Science and IT, RMIT University, Melbourne, Australia

    Behrooz Ghasemishabankareh & Xiaodong Li

  2. Department of Industrial Management, Vali-e-Asr University, Rafsanjan, Iran

    Nasser Shahsavari-Pour

  3. Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Kerman, Iran

    Mohammad-Ali Basiri

Authors
  1. Behrooz Ghasemishabankareh
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  2. Nasser Shahsavari-Pour
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  3. Mohammad-Ali Basiri
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  4. Xiaodong Li
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Corresponding author

Correspondence to Behrooz Ghasemishabankareh .

Editor information

Editors and Affiliations

  1. Canberra, Aust Capital Terr, Australia

    Tapabrata Ray

  2. University of New South Wales, Canberra, Aust Capital Terr, Australia

    Ruhul Sarker

  3. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

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Ghasemishabankareh, B., Shahsavari-Pour, N., Basiri, MA., Li, X. (2016). A Hybrid Imperialist Competitive Algorithm for the Flexible Job Shop Problem. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_19

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  • DOI: https://doi.org/10.1007/978-3-319-28270-1_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28269-5

  • Online ISBN: 978-3-319-28270-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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