Improved Beetle Swarm Optimization Algorithm for Flexible Job-shop Scheduling

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Improved Beetle Swarm Optimization Algorithm for Flexible Job-shop Scheduling

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[1]DING Kai,ZHAO Xinyue,LYU Jingxiang,et al.Improved Beetle Swarm Optimization Algorithm for Flexible Job-shop Scheduling[J].Journal of Zhengzhou University (Engineering Science),2024,45(03):111-118.[doi:10. 13705/ j. issn. 1671-6833. 2024. 03. 012]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 03 Page number: 111-118 Column: Public date: 2024-04-20

Title:: Improved Beetle Swarm Optimization Algorithm for Flexible Job-shop Scheduling

Author(s):: DING Kai; ZHAO Xinyue; LYU Jingxiang; ZHU Bin; Institute of Smart Manufacturing Systems, Chang􀆳an University, Xi􀆳an 710064, China

Keywords:: flexible job-shop scheduling; beetle swarm optimization; Levy flight; reverse search; adaptive parameter adjustment

CLC:: TH165TH18

DOI:: 10. 13705/ j. issn. 1671-6833. 2024. 03. 012

Abstract:: To solve the flexible job shop scheduling problem(FJSP), a hybrid Levy flight, reverse search, and parameter adaptive adjustment strategy improved beetle swarm optimization (LRA-BSO) was proposed based on the beetle antennae search algorithm which could simulate the foraging behavior of beetles in nature and the swarm intelligence optimization theory. Firstly, a FJSP model was established. Secondly, the initial population was generated based on the Tent chaotic mapping, which would improve the quality of the initial population. Then, the Levy flight strategy and reverse search strategy were used to improve the global search ability of the LRA-BSO algorithm, and the search step size and the search distance of the beetle swarm were adjusted through fitness feedback to avoid falling into local optimum. Finally, the optimization ability of the algorithm was validated through 6 multi-dimensional standard test functions. In addition, the applicability of the LRA-BSO algorithm in FJSP was verified by 10 standard test cases and 1 practical case. The test results showed that the algorithm performed better or equal to other intelligent optimization algorithms in eight standard test cases and demonstrated good optimization ability. In the practical cases, the improved algorithm had a 48% improvement in convergence speed compared to the original beetle swarm optimization algorithm.

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Last Update: 2024-04-29