A Modeling Method for Emergency Rescue Center Siting Based on the Variable Butterfly Optimization Algorithm
Selecting appropriate locations of emergency centers is an important issue in avoiding probable damages by natural disasters. Emergency rescue sites are constructed to provide emergency supplies swiftly for people in affected areas. Factors of transportation fluency and road damage degrees should be considered, which largely affect rescue efficiency. In order to find appropriate sites accurately, we proposed a redesigned method Variable Butterfly Optimization Algorithm (VBOA), based on the Butterfly Optimization Algorithm, by adding the Variation Operator mechanism to avoid the limitations of local optimum problems present in other optimization algorithms. The Variation Operator effectively combines both global and local search strategies to improve the performance of global searching, and it accelerates the convergence speed of the algorithm. We conducted our experiment on selected candidate sites with multiple optimization methods; the experiment results demonstrate that our proposed method maintains the balance between conditions of coverage area and expenditure. Our proposed method relieved the reliance of local optimum results and achieved better convergence accuracy in our selected samples in comparison with other methods both in initial and later siting phases.
Authors
- Yibo Sun (ORCID: https://orcid.org/0009-0004-8237-0650)
- Zhe Sun (ORCID: https://orcid.org/0000-0001-6694-2568)
- Hang Jin (ORCID: https://orcid.org/0000-0002-9744-1982)
- Lei Yue (ORCID: https://orcid.org/0000-0001-7189-3537)
- Weitong Chen
Institutions
- Nanjing University of Posts and Telecommunications (CN)
- The University of Adelaide (AU)
Publication Details
- Journal
- Electronics
- Published
- 2025-04-16
- DOI
- https://doi.org/10.3390/electronics14081606
- Primary Topic
- Robotic Path Planning Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Jiangsu Agricultural Science and Technology Innovation Fund