A fast and efficient discrete evolutionary algorithm for the uncapacitated facility location problem.

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. • The one direction mutation operator is proposed for UFLP. • A redundant checking strategy is proposed to optimize UFLP's solution. • An enhanced group-theory optimization algorithm is proposed for solving UFLP. • Experiment confirms the superiority of the new algorithm for UFLP. [ABSTRACT FROM AUTHOR]