A Comparative Study of Four Metaheuristic Algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for Evacuation Planning
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/a410d3de-8c16-49b7-a767-4f8812d8f4c9
- author
- Niyomubyeyi, Olive LU ; Sicuaio, Tome Eduardo LU ; Díaz González, José Ignacio ; Pilesjö, Petter LU and Mansourian, Ali LU
- organization
- publishing date
- 2020-01-03
- 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}}, }