The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Petter

Petter Pilesjö

Researcher

Petter

Evacuation planning optimization based on a multi-objective artificial bee colony algorithm

Author

  • Olive Niyomubyeyi
  • Petter Pilesjö
  • Ali Mansourian

Summary, in English

Evacuation is an important activity for reducing the number of casualties and amount of damage in disaster management. Evacuation planning is tackled as a spatial optimization problem. The decision-making process for evacuation involves high uncertainty, conflicting objectives, and spatial constraints. This study presents a Multi-Objective Artificial Bee Colony (MOABC) algorithm, modified to provide a better solution to the evacuation problem. The new approach combines random swap and random insertion methods for neighborhood search, the two-point crossover operator, and the Pareto-based method. For evacuation planning, two objective functions were considered to minimize the total traveling distance from an affected area to shelters and to minimize the overload capacity of shelters. The developed model was tested on real data from the city of Kigali, Rwanda. From computational results, the proposed model obtained a minimum fitness value of 5.80 for capacity function and 8.72 × 10 8 for distance function, within 161 s of execution time. Additionally, in this research we compare the proposed algorithm with Non-Dominated Sorting Genetic Algorithm II and the existing Multi-Objective Artificial Bee Colony algorithm. The experimental results show that the proposed MOABC outperforms the current methods both in terms of computational time and better solutions with minimum fitness values. Therefore, developing MOABC is recommended for applications such as evacuation planning, where a fast-running and efficient model is needed.

Department/s

  • Dept of Physical Geography and Ecosystem Science
  • Centre for Geographical Information Systems (GIS Centre)
  • BECC: Biodiversity and Ecosystem services in a Changing Climate
  • Centre for Advanced Middle Eastern Studies (CMES)
  • Middle Eastern Studies

Publishing year

2019-03-01

Language

English

Publication/Series

ISPRS International Journal of Geo-Information

Volume

8

Issue

3

Document type

Journal article

Publisher

MDPI AG

Topic

  • Geosciences, Multidisciplinary
  • Other Computer and Information Science
  • Physical Geography

Keywords

  • Evacuation planning
  • Geographic information system (GIS)
  • Multi-objective artificial bee colony
  • Spatial optimization
  • Swarm intelligence
  • Geospatial Artificial Intelligence (GeoAI)
  • Artificial Intelligence (AI)
  • Operational research

Status

Published

ISBN/ISSN/Other

  • ISSN: 2220-9964