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Improved Shuffled Frog Leaping Algorithm for Multi-objection Flexible Job-Shop Scheduling Problem

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Intelligent Computing and Internet of Things (ICSEE 2018, IMIOT 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 924))

Abstract

This paper proposes an improved shuffled frog leaping algorithm (SFLA-PSO) to solve multi-objective flexible job-shop problem. Pareto optimization strategy is used to balance three objectives including minimum the maximum completion time, maximum workload of all machines and the total processing time of all job. In the new algorithm, the particle swarm flight strategy is innovatively embedded into the local update operator of the SFLA-PSO. A dynamic crowding density sorting method is used to update external elite archive which holds the non-dominated population. To further improve the quality of the solution and the diversity of the population, a new local optimization strategy is developed associated with a neighborhood search strategy for achieving a high development ability of the new algorithm. The test results of benchmark instances illustrate the effectiveness of the new algorithm.

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Author information

Authors and Affiliations

  1. College of Mechanical Engineering, Chongqing University, Chongqing, 400044, China

    Mingli Gou, Qingxuan Gao & Su Yang

Authors
  1. Mingli Gou
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  2. Qingxuan Gao
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  3. Su Yang
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Corresponding author

Correspondence to Qingxuan Gao .

Editor information

Editors and Affiliations

  1. The University of Leeds, Leeds, UK

    Kang Li

  2. Shanghai University, Shanghai, China

    Minrui Fei

  3. Shanghai University, Shanghai, China

    Dajun Du

  4. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Zhile Yang

  5. Northeastern University, Shenyang, China

    Dongsheng Yang

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© 2018 Springer Nature Singapore Pte Ltd.

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Cite this paper

Gou, M., Gao, Q., Yang, S. (2018). Improved Shuffled Frog Leaping Algorithm for Multi-objection Flexible Job-Shop Scheduling Problem. In: Li, K., Fei, M., Du, D., Yang, Z., Yang, D. (eds) Intelligent Computing and Internet of Things. ICSEE IMIOT 2018 2018. Communications in Computer and Information Science, vol 924. Springer, Singapore. https://doi.org/10.1007/978-981-13-2384-3_1

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  • DOI: https://doi.org/10.1007/978-981-13-2384-3_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2383-6

  • Online ISBN: 978-981-13-2384-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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