Comparison of Parallel Simulated Annealing on SMP and Parallel Clusters for Planning a Drone’sRoute for Military Image Acquisition

E. Alsafi
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Abstract

Drones are vastly used in many civil and military applications. However, there are many factors to be highly considered in military applications.. In order to send a military drone with the aim of acquiring images from multiple sites, the mission time should be the least possible. Therefore, the minimum route plan is required.Simulated annealing (SA) algorithm is one of the metaheuristics selected to generate a feasible solution to solve this problem.This research exploits the parallelism in the simulated annealing with the aim of accelerating the time to find a suitable solution. Parallel programming divides the problem into smaller independent tasks, and then executes the sub-tasks simultaneously. Two parallel versions are therefore developed on different environment: synchronous SA on SMP, and asynchronous SA Complete Search Space (CSS) on parallel clusters. Experiments are conducted on the parallel clusters environment of the SANAM supercomputer. This research details the CSS, and compares it with the SMP SA developed in our previous study. Comparison is made in terms of speedup, efficiency, scalability, and quality of solution.
SMP并行模拟退火与并行聚类在无人机军事图像获取路径规划中的比较
无人机广泛应用于民用和军事领域。然而,在军事应用中有许多因素需要高度考虑。为了让军用无人机从多个地点获取图像,任务时间应该尽可能短。因此,需要最小路由规划。模拟退火(SA)算法是一种被选择的元启发式算法,以产生解决这一问题的可行解。本研究利用模拟退火中的并行性,以加快找到合适解的时间。并行编程将问题划分为较小的独立任务,然后同时执行子任务。因此,在不同的环境中开发了两个并行版本:SMP上的同步SA和并行集群上的异步SA完整搜索空间(CSS)。在SANAM超级计算机的并行集群环境下进行了实验。本研究详细介绍了CSS,并将其与我们之前研究开发的SMP SA进行了比较。在加速、效率、可伸缩性和解决方案质量方面进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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