基于智能搜索和特殊划分的人工蜂群算法
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阿坝州应用技术研究与开发资金项目(19YYJSYJ0025); 阿坝州应用技术研究与开发资金项目(19YYJSYJ0035)


Artificial bee colony with intellective search and special division
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he Application Technology Research and Development Project of Aba(19YYJSYJ0025);The Application Technology Research and Development Project of Aba(19YYJSYJ0035)

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    摘要:

    人工蜂群算法是一种具有强大搜索能力的全局搜索算法。传统的人工蜂群算法使用雇佣蜂、观察蜂和侦察蜂进行相互合作,每种蜜蜂有不同的分工,但不同类型的蜜蜂之间并没有差别。提出一种通过智能搜索和特殊划分来提升性能的人工蜂群算法。该算法中,不同的雇佣蜂和观察蜂会使用不同的搜索策略来寻找食物来源。该算法放弃了贪婪选择算法且在每次迭代时更新食物来源的位置。因此,该算法能够利用整个蜂群的经验来引导蜜蜂的搜索,通过一系列基准算法的性能分析证明了该算法的有效性。

    Abstract:

    The artificial bee colony (ABC) algorithm is a global search algorithm with powerful search capabilities. However, this algorithm uses a one-dimensional search and a greedy search strategy, which causes its convergence speed slower. The traditional ABC algorithm uses hiring bees, observing bees and scouting bees to cooperate with each other. Although each bee has a different division of labor, there is no difference between different types of bees. This paper proposes an artificial bee colony algorithm that improves performance through intelligent search and special partitioning. In this algorithm, different hire bees and observation bees use different search strategies to find food sources. In addition, we abandoned the greedy selection algorithm and updated the location of the food source on each iteration. In this case, the algorithm can use the experience of the entire bee colony to guide the bee’s search. And we prove its effectiveness through the experimental results of a series of benchmark algorithms.

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莫建麟,王玉晶.基于智能搜索和特殊划分的人工蜂群算法[J].重庆邮电大学学报(自然科学版),2020,32(6):1081-1087.

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  • 收稿日期:2018-12-27
  • 最后修改日期:2020-07-05
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  • 在线发布日期: 2020-12-23

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