Author(s)

Nan Jiang, Huizhen Zhang^*

Business School, University of Shanghai for Science and Technology, Shanghai, China.

The differential evolution algorithm is an evolutionary algorithm for global optimization and the un-capacitated facility location problem (UFL) is one of the classic NP-Hard problems. In this paper, combined with the specific characteristics of the UFL problem, we introduce the activation function to the algorithm for solving UFL problem and name it improved adaptive differential evolution algorithm (IADEA). Next, to improve the efficiency of the algorithm and to alleviate the problem of being stuck in a local optimum, an adaptive operator was added. To test the improvement of our algorithm, we compare the IADEA with the basic differential evolution algorithm by solving typical instances of UFL problem respectively. Moreover, to compare with other heuristic algorithm, we use the hybrid ant colony algorithm to solve the same instances. The computational results show that IADEA improves the performance of the basic DE and it outperforms the hybrid ant colony algorithm.

Un-Capacitated Facility Location Problem, Differential Evolution Algorithm, Adaptive Operator

Jiang, N. and Zhang, H. (2023) Improved Adaptive Differential Evolution Algorithm for the Un-Capacitated Facility Location Problem. Open Journal of Applied Sciences, 13, 685-695. doi: 10.4236/ojapps.2023.135054.