一种具有历史最佳点的改进人工蜂群算法

2018 Chinese Control And Decision Conference (CCDC) Pub Date : 2018-06-01 DOI:10.1109/CCDC.2018.8407519

Xingyu Xia, Xi Wang, Haidong Hu, Dongmei Wu, Hao Gao

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引用次数: 0

摘要

人工蜂群算法凭借强大的全局搜索能力，近年来受到越来越多的关注。但其缓慢的收敛速度制约了其发展。为了更好地平衡其探索和开发能力，我们定义了一个新的点，称为平均历史最佳点(MHB)，以引导蜜蜂种群的方向。在基本基准函数上的数值实验验证了算法的有效性。

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An improved artificial bee colony algorithm with history best points

Depending on the power global search ability, artificial bee colony algorithm attracts more attentions in recent years. But its slow convergence rate constraints its development. To better balance its exploration and exploitation abilities, we define a new point named as mean history best points (MHB) to lead the direction of bee population. The numerical experiments on the basic benchmark functions validate the efficiency of our algorithm.

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2018 Chinese Control And Decision Conference (CCDC)

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