A fast and efficient discrete evolutionary algorithm for the uncapacitated facility location problem. (1st March 2023)

• Record Type: Journal Article
• Authors: Zhang, Fazhan; He, Yichao; Ouyang, Haibin; Li, Wenben
• Is Part Of: Expert systems with applications. Volume 213:Part B(2023)
• Publication Date: 2023-03-01
• Subjects: Evolutionary algorithm -- Facility location problem -- Optimization algorithm -- One direction mutation operator -- Redundant checking strategy; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118978
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: 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. Highlights: 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.