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Abstract
This article proposed an improved gray wolf optimizer to deal with the flexible job-shop scheduling problem. By using a random key for coding job positions and adopting a local search strategy, we achieve group reconstruction and updating with the help of three good fitness values of the population, hence continuously searching for the optimal solution. Simulation experiments were conducted on a standardized test case, demonstrating the effectiveness of this method.
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