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Travelling Salesman Problem Solution Based-on Grey Wolf Algorithm over Hypercube Interconnection Network

机译:超立方体互连网络上基于灰狼算法的旅行商问题解决方案

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Travelling Salesman Problem (TSP) is one of the most popular NP-complete problems for the researches in the field of computer science which focused on optimization. TSP goal is to find the minimum path between cities with a condition of each city must to visit exactly once by the salesman. Grey Wolf Optimizer (GWO) is a new swarm intelligent optimization mechanism where it success in solving many optimization problems. In this paper, a parallel version of GWO for solving the TSP problem on a Hypercube Interconnection Network is presented. The algorithm has been compared to the alternative algorithms. Algorithms have been evaluated analytically and by simulations in terms of execution time, optimal cost, parallel runtime, speedup and efficiency. The algorithms are tested on a number of benchmark problems and found parallel Gray wolf algorithm is promising in terms of speed-up, efficiency and quality of solution in comparison with the alternative algorithms.
机译:旅行商问题(TSP)是计算机科学领域最关注优化的最流行的NP-完全问题之一。 TSP的目标是找到城市之间的最小路径,每个城市的条件是业务员必须精确地访问一次。灰狼优化器(GWO)是一种新型的群体智能优化机制,它可以成功解决许多优化问题。本文提出了一种并行版本的GWO,用于解决超立方体互连网络上的TSP问题。该算法已与替代算法进行了比较。通过执行时间,最佳成本,并行运行时间,加速和效率,对算法进行了分析和仿真评估。对这些算法进行了许多基准问题测试,发现与替代算法相比,并行灰狼算法在提高速度,效率和解决方案质量方面很有前途。

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