Petter Pilesjö
Professor
Multi-Objective Optimization Using Evolutionary Cuckoo Search Algorithm for Evacuation Planning
Author
Summary, in English
Proper emergency evacuation planning is a key to ensuring the safety and efficiency of resources allocation in disaster events. An efficient evacuation plan can save human lives and avoid other effects of disasters. To develop effective evacuation plans, this study proposed a multi-objective optimization model that assigns individuals to emergency shelters through safe evacuation routes during the available periods. The main objective of the proposed model is to minimize the total travel distance of individuals leaving evacuation zones to shelters, minimize the risk on evacuation routes and minimize the overload of shelters. The experimental results show that the Discrete Multi-Objective Cuckoo Search (DMOCS) has better and consistent performance as compared to the standard Multi-Objective Cuckoo Search (MOCS) in most cases in terms of execution time; however, the performance of MOCS is still within acceptable ranges. Metrics and measures such as hypervolume indicator, convergence evaluation and parameter tuning have been applied to evaluate the quality of Pareto front and the performance of the proposed algorithm. The results showed that the DMOCS has better performance than the standard MOCS.
Department/s
- Dept of Physical Geography and Ecosystem Science
- Centre for Geographical Information Systems (GIS Centre)
- Centre for Advanced Middle Eastern Studies (CMES)
Publishing year
2022-02-15
Language
English
Pages
53-75
Publication/Series
Geomatics
Volume
2
Issue
1
Document type
Journal article
Publisher
MDPI AG
Topic
- Computer and Information Science
- Physical Geography
Keywords
- emergency evacuation planning
- emergency evacuation planning; multi-objective optimization
- MOCS algorithm
- GIS
- Operational research
- Geospatial Artificial Intelligence (GeoAI)
- Artificial Intelligence (AI)
Status
Published
ISBN/ISSN/Other
- ISSN: 2673-7418