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A Multiple Pheromone Table Based Ant Colony Optimization for Clustering

机译：基于多信息素表的蚁群算法聚类

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摘要

Ant colony optimization (ACO) is an efficient heuristic algorithm for combinatorial optimization problems, such as clustering. Because the search strategy of ACO is similar to those of other well-known heuristics, the probability of searching particular regions will be increased if better results are found and kept. Although this kind of search strategy may find a better approximate solution, it also has a high probability of losing the potential search directions. To prevent the ACO from losing too many potential search directions at the early iterations, a novel pheromone updating strategy is presented in this paper. In addition to the "original" pheromone table used to keep track of the promising information, a second pheromone table is added to the proposed algorithm to keep track of the unpromising information so as to increase the probability of searching directions worse than the current solutions. Several well-known clustering datasets are used to evaluate the performance of the proposed method in this paper. The experimental results show that the proposed method can provide better results than ACO and other clustering algorithms in terms of quality.
机译：蚁群优化（ACO）是一种有效的启发式算法，用于解决组合优化问题，例如聚类。由于ACO的搜索策略与其他众所周知的启发式搜索策略相似，因此，如果找到并保留更好的结果，则搜索特定区域的可能性将会增加。尽管这种搜索策略可以找到更好的近似解决方案，但也很有可能失去潜在的搜索方向。为了防止ACO在早期迭代中丢失太多潜在的搜索方向，本文提出了一种新颖的信息素更新策略。除了用于跟踪有希望的信息的“原始”信息素表之外，在所提出的算法中还添加了第二个信息素表，以跟踪不希望的信息，从而增加了搜索方向的可能性比当前解决方案差。本文使用了几个著名的聚类数据集来评估该方法的性能。实验结果表明，该方法在质量上比ACO和其他聚类算法能提供更好的结果。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第11期|158632.1-158632.11|共11页
  • 作者

    Hu Kai-Cheng; Tsai Chun-Wei; Chiang Ming-Chao; Yang Chu-Sing;

  • 作者单位

    Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 80424, Taiwan.;

    Natl Ilan Univ, Dept Comp Sci & Informat Engn, Yilan 26047, Taiwan.;

    Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 80424, Taiwan.;

    Natl Cheng Kung Univ, Inst Comp & Commun Engn, Tainan 70101, Taiwan.;

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