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A Comparative Study of Four Metaheuristic Algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for Evacuation Planning

Niyomubyeyi, Olive LU ; Sicuaio, Tome Eduardo LU ; Díaz González, José Ignacio ; Pilesjö, Petter LU and Mansourian, Ali LU (2020) In Algorithms 13(1).
Abstract
Evacuation planning is an important activity in disaster management to reduce the effects of disasters on urban communities. It is regarded as a multi-objective optimization problem that involves conflicting spatial objectives and constraints in a decision-making process. Such problems are difficult to solve by traditional methods. However, metaheuristics methods have been shown to be proper solutions. Well-known classical metaheuristic algorithms—such as simulated annealing (SA), artificial bee colony (ABC), standard particle swarm optimization (SPSO), genetic algorithm (GA), and multi-objective versions of them—have been used in the spatial optimization domain. However, few types of research have applied these classical methods, and... (More)
Evacuation planning is an important activity in disaster management to reduce the effects of disasters on urban communities. It is regarded as a multi-objective optimization problem that involves conflicting spatial objectives and constraints in a decision-making process. Such problems are difficult to solve by traditional methods. However, metaheuristics methods have been shown to be proper solutions. Well-known classical metaheuristic algorithms—such as simulated annealing (SA), artificial bee colony (ABC), standard particle swarm optimization (SPSO), genetic algorithm (GA), and multi-objective versions of them—have been used in the spatial optimization domain. However, few types of research have applied these classical methods, and their performance has not always been well evaluated, specifically not on evacuation planning problems. This research applies the multi-objective versions of four classical metaheuristic algorithms (AMOSA, MOABC, NSGA-II, and MSPSO) on an urban evacuation problem in Rwanda in order to compare the performances of the four algorithms. The performances of the algorithms have been evaluated based on the effectiveness, efficiency, repeatability, and computational time of each algorithm. The results showed that in terms of effectiveness, AMOSA and MOABC achieve good quality solutions that satisfy the objective functions. NSGA-II and MSPSO showed third and fourth-best effectiveness. For efficiency, NSGA-II is the fastest algorithm in terms of execution time and convergence speed followed by AMOSA, MOABC, and MSPSO. AMOSA, MOABC, and MSPSO showed a high level of repeatability compared to NSGA-II. It seems that by modifying MOABC and increasing its effectiveness, it could be a proper algorithm for evacuation planning. (Less)
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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Geospatial Artificial Intelligence (GeoAI), Artificial Intelligence (AI)
in
Algorithms
volume
13
issue
1
article number
16
pages
21 pages
publisher
MDPI AG
external identifiers
  • scopus:85078702401
ISSN
1999-4893
DOI
10.3390/a13010016
language
English
LU publication?
yes
id
a410d3de-8c16-49b7-a767-4f8812d8f4c9
date added to LUP
2020-02-11 12:24:24
date last changed
2023-10-08 00:25:03
@article{a410d3de-8c16-49b7-a767-4f8812d8f4c9,
  abstract     = {{Evacuation planning is an important activity in disaster management to reduce the effects of disasters on urban communities. It is regarded as a multi-objective optimization problem that involves conflicting spatial objectives and constraints in a decision-making process. Such problems are difficult to solve by traditional methods. However, metaheuristics methods have been shown to be proper solutions. Well-known classical metaheuristic algorithms—such as simulated annealing (SA), artificial bee colony (ABC), standard particle swarm optimization (SPSO), genetic algorithm (GA), and multi-objective versions of them—have been used in the spatial optimization domain. However, few types of research have applied these classical methods, and their performance has not always been well evaluated, specifically not on evacuation planning problems. This research applies the multi-objective versions of four classical metaheuristic algorithms (AMOSA, MOABC, NSGA-II, and MSPSO) on an urban evacuation problem in Rwanda in order to compare the performances of the four algorithms. The performances of the algorithms have been evaluated based on the effectiveness, efficiency, repeatability, and computational time of each algorithm. The results showed that in terms of effectiveness, AMOSA and MOABC achieve good quality solutions that satisfy the objective functions. NSGA-II and MSPSO showed third and fourth-best effectiveness. For efficiency, NSGA-II is the fastest algorithm in terms of execution time and convergence speed followed by AMOSA, MOABC, and MSPSO. AMOSA, MOABC, and MSPSO showed a high level of repeatability compared to NSGA-II. It seems that by modifying MOABC and increasing its effectiveness, it could be a proper algorithm for evacuation planning.}},
  author       = {{Niyomubyeyi, Olive and Sicuaio, Tome Eduardo and Díaz González, José Ignacio and Pilesjö, Petter and Mansourian, Ali}},
  issn         = {{1999-4893}},
  keywords     = {{Geospatial Artificial Intelligence (GeoAI); Artificial Intelligence (AI)}},
  language     = {{eng}},
  month        = {{01}},
  number       = {{1}},
  publisher    = {{MDPI AG}},
  series       = {{Algorithms}},
  title        = {{A Comparative Study of Four Metaheuristic Algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for Evacuation Planning}},
  url          = {{http://dx.doi.org/10.3390/a13010016}},
  doi          = {{10.3390/a13010016}},
  volume       = {{13}},
  year         = {{2020}},
}