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Parallel bat algorithm for optimizing makespan in job shop scheduling problems

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Abstract

Parallel processing plays an important role in efficient and effective computations of function optimization. In this paper, an optimization algorithm based on parallel versions of the bat algorithm (BA), random-key encoding scheme, communication strategy scheme and makespan scheme is proposed to solve the NP-hard job shop scheduling problem. The aim of the parallel BA with communication strategies is to correlate individuals in swarms and to share the computation load over few processors. Based on the original structure of the BA, the bat populations are split into several independent groups. In addition, the communication strategy provides the diversity-enhanced bats to speed up solutions. In the experiment, forty three instances of the benchmark in job shop scheduling data set with various sizes are used to test the behavior of the convergence, and accuracy of the proposed method. The results compared with the other methods in the literature show that the proposed scheme increases more the convergence and the accuracy than BA and particle swarm optimization.

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Authors and Affiliations

  1. Department of Electronics Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan, ROC

    Thi-Kien Dao, Tien-Szu Pan & Trong-The Nguyen

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, China

    Jeng-Shyang Pan

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  1. Thi-Kien Dao
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  2. Tien-Szu Pan
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Correspondence to Jeng-Shyang Pan.

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Dao, TK., Pan, TS., Nguyen, TT. et al. Parallel bat algorithm for optimizing makespan in job shop scheduling problems. J Intell Manuf 29, 451–462 (2018). https://doi.org/10.1007/s10845-015-1121-x

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