Abstract
Evacuation is an important activity for reducing the number of casualties and amount of damage in disaster management. Evacuation planning is tackled as a spatial optimization problem. The decision-making process for evacuation involves high uncertainty, conflicting objectives, and spatial constraints. This study presents a Multi-Objective Artificial Bee Colony (MOABC) algorithm, modified to provide a better solution to the evacuation problem. The new approach combines random swap and random insertion methods for neighborhood search, the two-point crossover operator, and the Pareto-based method. For evacuation planning, two objective functions were considered to minimize the total traveling distance from an affected area to shelters and to minimize the overload capacity of shelters. The developed model was tested on real data from the city of Kigali, Rwanda. From computational results, the proposed model obtained a minimum fitness value of 5.80 for capacity function and 8.72 × 108 for distance function, within 161 s of execution time. Additionally, in this research we compare the proposed algorithm with Non-Dominated Sorting Genetic Algorithm II and the existing Multi-Objective Artificial Bee Colony algorithm. The experimental results show that the proposed MOABC outperforms the current methods both in terms of computational time and better solutions with minimum fitness values. Therefore, developing MOABC is recommended for applications such as evacuation planning, where a fast-running and efficient model is needed.
Highlights
The increase in the frequency of natural disasters such as earthquakes, landslides, and floods is becoming a critical problem globally due to their effects on humans and the environment [1]
From the investigation of the results, we found that the variance for capacity function ranged between 0.051 and 0.057, and it ranged between 0.080 and 0.086 for distance function (Table 4)
It was concluded that the average of repeatability of the proposed Multi-Objective Artificial Bee Colony (MOABC) is about 93%
Summary
The increase in the frequency of natural disasters such as earthquakes, landslides, and floods is becoming a critical problem globally due to their effects on humans and the environment [1]. Metaheuristic algorithms are known to be efficient for solving more complex problems by providing a set of optimal solutions in a reasonable amount of time [13], without being influenced by the preferences of experts. In the spatial optimization domain, many studies applied these techniques due to their potential to optimize multiple and conflicting objectives and provide non-dominated solutions as outputs [13] These methods include Multi-objective linear programming [22], Genetic Algorithm (GA) [23,24,25,26], Particle Swarm Optimization (PSO) [27], Ant Colony Optimization (ACO) [28], Tabu Search [29,30], and the Artificial Bee Colony (ABC) [31].
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