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Solving Traveling Salesman Problem by Using Combinatorial Artificial Bee Colony Algorithms

机译:通过使用组合人工蜂殖民地算法来解决旅行推销员问题

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Artificial bee colony (ABC) is a quite popular optimization approach that has been used in many fields, with its not only standard form but also improved versions. In this paper, new versions of ABC algorithm to solve TSP are introduced and described in detail. One of these is the combinatorial version of standard ABC, called combinatorial ABC (CABC) algorithm. The other one is an improved version of CABC algorithm, called quick CABC (qCABC) algorithm. In order to see the efficiency of the new versions, 15 different TSP benchmarks are considered and the results generated are compared with different well-known optimization methods. The simulation results show that, both CABC and qCABC algorithms demonstrate good performance for TSP and also the new definition in quick ABC (qABC) improves the convergence performance of CABC on TSP.
机译:人造蜜蜂殖民地(ABC)是一种非常受欢迎的优化方法,已在许多领域中使用,其不仅具有标准形式,而且还具有改进的版本。 在本文中,详细介绍了求解TSP的ABC算法的新版本。 其中一个是标准ABC的组合版本,称为组合ABC(CABC)算法。 另一个是CABC算法的改进版本,称为快速CABC(QCABC)算法。 为了查看新版本的效率,考虑了15个不同的TSP基准,并将产生的结果与不同众所周知的优化方法进行比较。 仿真结果表明,CABC和QCABC算法都表现出对TSP的良好性能,而且快速ABC(QABC)的新定义也提高了TSP上CABC的收敛性能。

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