Optimized Location-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS

• Published in: ISPRS international journal of geo-information Vol. 7; no. 8; p. 292
• Main Authors: Saeidian, Bahram, Mesgari, Mohammad Saadi, Pradhan, Biswajeet, Ghodousi, Mostafa
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2018
• Subjects: ACO; Adequacy; Algorithms; Ant colony optimization; Artificial intelligence; Clustering; Cybernetics; Disaster management; disaster preparedness; Disasters; Earthquakes; Emergency preparedness; Genetic algorithms; Geodetics; Geographic information systems; Geographical information systems; GIS; Heuristic; Heuristic methods; Information systems; Initial conditions; International conferences; location-allocation; Optimization techniques; Particle swarm optimization; Problems; PSO; Seismic activity; Site selection; system optimization; TOPSIS; Iran; United States > US
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi7080292

After an earthquake, it is required to establish temporary relief centers in order to help the victims. Selection of proper sites for these centers has a significant effect on the processes of urban disaster management. In this paper, the location and allocation of relief centers in district 1 of Tehran are carried out using Geospatial Information System (GIS), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision model, a simple clustering method and the two meta-heuristic algorithms of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). First, using TOPSIS, the proposed clustering method and GIS analysis tools, sites satisfying initial conditions with adequate distribution in the area are chosen. Then, the selection of proper centers and the allocation of parcels to them are modelled as a location/allocation problem, which is solved using the meta-heuristic optimization algorithms. Also, in this research, PSO and ACO are compared using different criteria. The implementation results show the general adequacy of TOPSIS, the clustering method, and the optimization algorithms. This is an appropriate approach to solve such complex site selection and allocation problems. In view of the assessment results, the PSO finds better answers, converges faster, and shows higher consistency than the ACO.