.png){kind=icon}
返回
A fast and efficient discrete evolutionary algorithm for the uncapacitated facility location problem
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2022.118978.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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.
Keyword:
Evolutionary algorithm
Facility location problem
Optimization algorithm
One direction mutation operator
Redundant checking strategy
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
分享收藏
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏Binary grey wolf optimization approaches for feature selection基于二进制灰狼优化的特征选择方法.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏A genetic algorithm for the uncapacitated single allocation planar hub location problem无资本单分配平面枢纽选址问题的遗传算法
PREAI
分享收藏
查看更多内容