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首页> 外文期刊>International journal of advanced pervasive and ubiquitous computing >Research on Ant Colony Optimization With Tabu Search Ability
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Research on Ant Colony Optimization With Tabu Search Ability

机译：禁忌搜索能力蚁群优化研究

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TSP (traveling salesman problem) is a classical problem in combinatorial optimization. It's not totally solved; the route number and the number of cities has increased exponentially, so we couldn't find the best solution easily. This paper does a lot research of tabu search (TS) besides AA and proposes a new algorithm. Making use of TS's advantages, the new proposed algorithm's performance is meliorated. Firstly, aiming at solving AA's slow convergence, the authors increase the pheromone of the best route, decrease the pheromone of the worst route, to increase the conductive ability of the pheromone to the algorithm. Secondly, aiming at solving AA's being premature, this paper introduces TS into AS's every iteration. The TS can help the algorithm find a better solution. So, the new algorithm's convergence speed is quickened, and its performance is improved. At last, this paper applied the algorithm to the traveling salesman problem to test its performances. The simulation results show that the new algorithm could find optimum solutions more effectively in time and quantity.
机译：TSP（旅行推销员问题）是组合优化中的经典问题。它没有完全解决;路线号码和城市数量呈指数增加，因此我们无法轻松找到最佳解决方案。本文除了AA之外，对禁忌搜索（TS）进行了大量研究，并提出了一种新的算法。利用TS的优势，新的算法的性能提出。首先，旨在解决AA的缓慢收敛性，作者增加了最佳路线的信息素，降低最差路径的信息素，以提高信息素到算法的导电能力。其次，旨在解决AA的早产，本文介绍了每次迭代时的TS。 TS可以帮助算法找到更好的解决方案。因此，新的算法的收敛速度被加速，其性能提高。最后，本文将该算法应用于旅行推销员问题以测试其性能。仿真结果表明，新算法可以更有效地找到最佳解决方案及数量。

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