Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Multi-Objective Optimization Using Evolutionary Cuckoo Search Algorithm for Evacuation Planning

Sicuaio, Tomé LU ; Niyomubyeyi, Olive LU ; Shyndyapin, Andrey ; Pilesjö, Petter LU and Mansourian, A LU (2022) In Geomatics 2(1). p.53-75
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
emergency evacuation planning, emergency evacuation planning; multi-objective optimization, MOCS algorithm, GIS, Operational research, Geospatial Artificial Intelligence (GeoAI), Artificial Intelligence (AI)
in
Geomatics
volume
2
issue
1
pages
22 pages
publisher
MDPI AG
ISSN
2673-7418
DOI
10.3390/geomatics2010005
language
English
LU publication?
yes
id
38b2a0f7-f0b3-454b-a4b4-055ceea5373a
date added to LUP
2022-03-23 17:08:53
date last changed
2023-08-30 12:38:52
@article{38b2a0f7-f0b3-454b-a4b4-055ceea5373a,
  abstract     = {{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.}},
  author       = {{Sicuaio, Tomé and Niyomubyeyi, Olive and Shyndyapin, Andrey and Pilesjö, Petter and Mansourian, A}},
  issn         = {{2673-7418}},
  keywords     = {{emergency evacuation planning; emergency evacuation planning; multi-objective optimization; MOCS algorithm; GIS; Operational research; Geospatial Artificial Intelligence (GeoAI); Artificial Intelligence (AI)}},
  language     = {{eng}},
  month        = {{02}},
  number       = {{1}},
  pages        = {{53--75}},
  publisher    = {{MDPI AG}},
  series       = {{Geomatics}},
  title        = {{Multi-Objective Optimization Using Evolutionary Cuckoo Search Algorithm for Evacuation Planning}},
  url          = {{http://dx.doi.org/10.3390/geomatics2010005}},
  doi          = {{10.3390/geomatics2010005}},
  volume       = {{2}},
  year         = {{2022}},
}