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

1
Department of Physical Geography and Ecosystem Science, Lund University, SE-221 00 Lund, Sweden
2
Center for Geographic Information Systems and Remote Sensing, College of Science and Technology, University of Rwanda, Kigali 3900, Rwanda
3
Center for Middle Eastern Studies, Lund University, SE-221 00 Lund, Sweden
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(1), 16; https://doi.org/10.3390/a13010016
Received: 26 November 2019 / Revised: 19 December 2019 / Accepted: 26 December 2019 / Published: 3 January 2020
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. View Full-Text
Keywords: evacuation planning; multi-objective optimization; meta-heuristic algorithms; AMOSA; MOABC; MSPSO; NSGA-II evacuation planning; multi-objective optimization; meta-heuristic algorithms; AMOSA; MOABC; MSPSO; NSGA-II
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Niyomubyeyi, O.; Sicuaio, T.E.; Díaz González, J.I.; Pilesjö, P.; Mansourian, A. A Comparative Study of Four Metaheuristic Algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for Evacuation Planning. Algorithms 2020, 13, 16.

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