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.
Highlights
IntroductionClimate changes as well as environmental changes—e.g., deforestation—increase the frequency and intensity of natural disasters such as hurricanes, floods, and landslides [1]
Natural disasters are threats to human life and the ecosystem in general
This paper aims to compare the performance of four metaheuristic algorithms extended from the standard algorithms of simulated annealing, artificial bee colony, genetic algorithm, and particle swarm optimization for evacuation planning
Summary
Climate changes as well as environmental changes—e.g., deforestation—increase the frequency and intensity of natural disasters such as hurricanes, floods, and landslides [1]. Such extreme catastrophes cause many losses in lives, affect the economy, and leave many damages to the affected area. A critical challenge in evacuation planning is to find optimum evacuation time and a proper shelter allocation such that they have enough space for evacuees and other basic living requirements This means that evacuation planning is considered as a complex multi-criteria decision problem with conflicting objectives, constraints, and spatial aspects. Such problems are modeled by multi-objective optimization techniques, which often provide decision-makers with quick responses and reliable solutions to the problem
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