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Discrete Particle Swarm Optimization for Solving a Single to Multiple Destinations in Evacuation Planning

机译:离散粒子群优化算法在疏散计划中解决单个到多个目的地

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During the evacuation process, the most challenging task is to move people to safer locations. As time is the decision factor in the evacuation process, urgent and firmly decisions are required. An evacuation plan should be efficiently constructed by taking into account the routes of vehicle. This paper explored the feasibility of using a discrete particle swarm optimization (DPSO) algorithm to solve evacuation vehicle routing problem (EVRP) problem focusing on a static vehicle routing. A linear mathematical formulation is constructed with the objective to find a minimum total travelling of the capacitated vehicles from vehicle location to various number flooded areas. A solution representation based on a search decomposition procedure is proposed to accommodate the routing process and mapped to the discrete multi-valued particle in DPSO. Computational experiment involves datasets from the event of flash flood. Comparative analyses were carried on both DPSO and GA. The results indicate that the DPSO are highly competitive and showed good performance in both fitness value (total travelling time) and processing time.
机译:在疏散过程中,最具挑战性的任务是将人员转移到更安全的位置。由于时间是疏散过程中的决定因素,因此需要紧急而坚定的决定。疏散计划应通过考虑车辆的路线来有效地制定。本文探讨了使用离散粒子群优化(DPSO)算法解决以静态车辆路径为中心的疏散车辆路径问题(EVRP)的可行性。构造线性数学公式,其目的是找到有能力的车辆从车辆位置到不同数量的洪水区域的最小总行驶距离。提出了一种基于搜索分解过程的解决方案表示,以适应路由过程并将其映射到DPSO中的离散多值粒子。计算实验涉及暴洪事件的数据集。对DPSO和GA进行了比较分析。结果表明,DPSO具有很高的竞争力,并且在适应度值(总旅行时间)和处理时间上均表现出良好的性能。

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