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首页> 外文期刊>Future generation computer systems >Implementation of a parallel Genetic Algorithm on a cluster of workstations: Traveling Salesman Problem, a case study
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Implementation of a parallel Genetic Algorithm on a cluster of workstations: Traveling Salesman Problem, a case study

机译：在工作站集群上并行遗传算法的实现：旅行商问题，案例研究

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A parallel version of a Genetic Algorithm (GA) is presented and implemented on a cluster of workstations. Even though our algorithm is general enough to be applied to a wide variety of problems, we used it to obtain optimal and/or suboptimal solutions to the well-known Traveling Salesman Problem. The proposed algorithm is implemented using the Parallel Virtual Machine (PVM) library over a network of workstations. A master-slave paradigm is used to implement the proposed parallel/distributed Genetic Algorithm (PDGA), which is based on a distributed--memory approach. Tests were performed with clusters of 1. 2, 4, 8, and 16 workstations, using several real problems and population sizes. Results are presented to show how the performance of the algorithm is affected by variations on the number of slaves, population size, mutation rate, and mutation interval. The results presented show the utility, versatility, efficiency and potential value of the proposed parallel and distributed Genetic Algorithm to tackle NP-complete problems of the same nature.
机译：提出了遗传算法（GA）的并行版本，并在工作站集群上实现。即使我们的算法足够通用，可以应用于各种各样的问题，我们还是使用它来获得针对著名的旅行商问题的最优和/或次优解决方案。所提出的算法是通过工作站网络上的并行虚拟机（PVM）库实现的。主从范式用于实现所提出的基于分布式内存方法的并行/分布式遗传算法（PDGA）。测试使用了1个，2个，4个，8个和16个工作站的集群，并使用了几个实际问题和总体规模。结果表明，该算法的性能如何受到奴隶数量，种群大小，变异率和变异间隔的变化的影响。结果表明，所提出的并行和分布式遗传算法在解决相同性质的NP完全问题上具有实用性，多功能性，效率和潜在价值。

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