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A fast and efficient discrete evolutionary algorithm for the uncapacitated facility location problem
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AI
DOI：10.1016/j.eswa.2022.118978.png)
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Abstract
En 中文
In order to solve the uncapacitated facility location problem (UFLP) quickly and effectively, an enhanced group theory-based optimization algorithm (EGTOA) is proposed in this paper. Firstly, a new local search operator, One Direction Mutation Operator, is proposed, which is suitable for solving UFLP. Secondly, a Redundant Checking Strategy is presented to further optimize the quality of feasible solutions. To verify the performance of EGTOA, 15 benchmark instances of UFLP is selected in OR-Library, the comparison results with the 16 existing algorithms show that the solution obtained by EGTOA is better than other algorithms, moreover its speed is much faster than state-of-the-art algorithms. These demonstrates that EGTOA is a fast and effective algorithm for solving UFLP.
Keywords:
Evolutionary algorithm
Facility location problem
Optimization algorithm
One direction mutation operator
Redundant checking strategy
Journal
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2.9W
Citations:
10.2W