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.

Automated summary

An enhanced group theory-based optimization algorithm (EGTOA) is proposed to solve the uncapacitated facility location problem (UFLP) quickly and effectively. By introducing a new local search operator and a redundant checking strategy, EGTOA outperforms existing algorithms in terms of solution quality and speed.