AHOA: Adaptively Hybrid Optimization Algorithm for Flexible Job-shop Scheduling Problem

Ye, Jiaxin; Xu, Dawen; Hong, Haokai; Lai, Yongxuan; Jiang, Min

doi:10.1007/978-3-030-95384-3_18

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…Xin et al [13] investigated the FJSSP with AGV strategy utilizing multiple genetic algorithms. Ye et al [14] proposed an adaptive hybrid optimization method employing reinforcement learning. Wang et al [15] proposed an invasive weed optimization algorithm embedded based on the gray wolf algorithm, which was utilized to enhance the capabilities of the global and local search and enhance the quality of the initial solution.…”

Section: Introductionmentioning

confidence: 99%

Application of Improved Sparrow Search Algorithm to Flexible Job Shop Scheduling Problem

Long-Yan Xu

2024

jes

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When the reality of energy saving and reduction of enterprises’ emissions are a concern, research on the investigation of establishing mathematical models to optimize the production time has been studied. In this article, an enhanced sparrow optimization method is proposed. First, two-layer coding is employed for workpieces and machines according to the model requirements. Secondly, the three-dimensional chaotic mapping scheme is presented to improve the population heterogeneity of the algorithm, and the adaptive inertia weight balance algorithm is implemented to offset the speed of the convergence and its probability. Finally, the Cauchy mutation scheme is adopted to help the algorithm jump out of the local optimum. Simulated data is run to check the superiority of the proposed method. So, through the simulations and comparisons of 10 kinds of test datasets, the outcomes suggest that the solution quality of the enhanced sparrow optimization method has been effectively advanced, and its good global optimization ability is shown, which can provide scheduling strategies for workshop productions. One of the successes of the ISSA algorithm is its superior search accuracy.

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Section: Introductionmentioning

confidence: 99%