Abstract
In this paper, a hybrid whale optimization algorithm (HWOA) with local search strategy is proposed to minimize the makespan for the Open-shop Scheduling Problem (OSP), which is one of the most important scheduling types in practical applications. Firstly, the large-order-value (LOV) encoding rule is presented to transform HWOA’s individuals from continuous vectors into job permutations, which makes HWOA suitable for dealing with the OSP and performing global search in the solution space. Secondly, a local search mechanism guided by different neighborhoods is designed to enhance the search depth in the promising regions of excellent solutions. Computational experiments and comparisons show that HWOA performs well on random generation problems. This is the first time that HWOA has been used to address the OSP.
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