A novel parallelization method of genetic algorithm (GA) solution of the Traveling Salesman Problem (TSP) is presented. The proposed method can considerably accelerate the solution of the equivalent TSP of many complex vehicle routing problems (VRPs) in the cloud implementation of intelligent transportation systems. The solution provides routing information besides all the services required by the autonomous vehicles in vehicular clouds. GA is considered as an important class of evolutionary algorithms that can solve optimization problems in growing intelligent transport systems. But, to meet time criteria in time-constrained problems of intelligent transportation systems like routing and controlling the autonomous vehicles, a highly parallelizable GA is needed. The proposed method parallelizes the GA by designing three concurrent kernels, each of which running some dependent effective operators of GA. It can be straightforwardly adapted to run on many-core and multi-core processors. To best use the valuable resources of such processors in parallel execution of the GA, threads that run any of the triple kernels are synchronized by a low-cost switching mechanism. The proposed method was experimented for parallelizing a GA-based solution of TSP over multi-core and many-core systems. The results confirm the efficiency of the proposed method for parallelizing GAs on many-core as well as on multi-core systems.

中文翻译：

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智能交通系统云实现中车辆路径问题的高效并行遗传算法解决方案

提出了旅行商问题（TSP）的遗传算法（GA）求解的并行化方法。所提出的方法可以在智能交通系统的云实施中大大加快解决许多复杂车辆路径问题（VRP）的等效TSP的速度。该解决方案除了提供车辆云中自动驾驶车辆所需的所有服务外，还提供路线信息。遗传算法被认为是一类重要的进化算法，可以解决日益增长的智能运输系统中的优化问题。但是，要在诸如路线图和控制自动驾驶车辆等智能交通系统的时间受限问题中满足时间标准，就需要高度可并行化的GA。所提出的方法通过设计三个并发内核来并行化GA，每个都运行一些依赖的有效GA运算符。它可以直接修改为在多核和多核处理器上运行。为了在GA并行执行中最好地利用此类处理器的宝贵资源，运行任何三重内核的线程都将通过低成本的切换机制进行同步。对所提出的方法进行了实验，以在多核和多核系统上并行化基于GA的TSP解决方案。结果证实了提出的方法在多核以及多核系统上并行化GA的效率。运行任何三重内核的线程都通过低成本的切换机制进行同步。对所提出的方法进行了实验，以在多核和多核系统上并行化基于GA的TSP解决方案。结果证实了提出的方法在多核以及多核系统上并行化GA的效率。运行任何三重内核的线程都通过低成本的切换机制进行同步。对所提出的方法进行了实验，以在多核和多核系统上并行化基于GA的TSP解决方案。结果证实了提出的方法在多核以及多核系统上并行化GA的效率。