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A Hybrid Particle Swarm Optimization Algorithm Enhanced with Nonlinear Inertial Weight and Gaussian Mutation for Job Shop Scheduling Problems

Author

Listed:
  • Hongli Yu

    (School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China)

  • Yuelin Gao

    (Ningxia Province Key Laboratory of Intelligent Information and Data Processing, North Minzu University, Yinchuan 750021, China)

  • Le Wang

    (School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China)

  • Jiangtao Meng

    (School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China)

Abstract

Job shop scheduling problem (JSSP) has high theoretical and practical significance in academia and manufacturing respectively. Therefore, scholars in many different fields have been attracted to study this problem, and many meta-heuristic algorithms have been proposed to solve this problem. As a meta-heuristic algorithm, particle swarm optimization (PSO) has been used to optimize many practical problems in industrial manufacturing. This paper proposes a hybrid PSO enhanced with nonlinear inertia weight and and Gaussian mutation (NGPSO) to solve JSSP. Nonlinear inertia weight improves local search capabilities of PSO, while Gaussian mutation strategy improves the global search ability of NGPSO, which is beneficial to the population to maintain diversity and reduce probability of the algorithm falling into the local optimal solution. The proposed NGPSO algorithm is implemented to solve 62 benchmark instances of JSSP, and the experimental results are compared with other algorithms. The results obtained by analyzing the experimental data show that the algorithm is better than other comparison algorithms in solving JSSP.

Suggested Citation

  • Hongli Yu & Yuelin Gao & Le Wang & Jiangtao Meng, 2020. "A Hybrid Particle Swarm Optimization Algorithm Enhanced with Nonlinear Inertial Weight and Gaussian Mutation for Job Shop Scheduling Problems," Mathematics, MDPI, vol. 8(8), pages 1-17, August.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1355-:d:398458
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    References listed on IDEAS

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