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Artificial Bee Colony Training of Neural Networks

机译：神经网络的人造蜜蜂殖民地培训

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The Artificial Bee Colony (ABC) is a recently introduced swarm intelligence algorithm for optimization, that has previously been applied successfully to the training of neural networks. This paper explores more carefully the performance of the ABC algorithm for optimizing the connection weights of feed-forward neural networks for classification tasks, and presents a more rigorous comparison with the traditional Back-Propagation (BP) training algorithm. The empirical results show that using the standard "stopping early" approach with optimized learning parameters leads to improved BP performance over the previous comparative study, and that a simple variation of the ABC approach provides improved ABC performance too. With both improvements applied, we conclude that the ABC approach does perform very well on small problems, but the generalization performances achieved are only significantly better than standard BP on one out of six datasets, and the training times increase rapidly as the size of the problem grows.
机译：人造蜜蜂殖民地（ABC）是最近引入的优化智能算法，以前已成功应用于神经网络的培训。本文更仔细地探讨了ABC算法的性能，以优化用于分类任务的前馈神经网络的连接权重，并与传统的背传播（BP）训练算法提供更严格的比较。经验结果表明，使用具有优化学习参数的标准“停止早期”方法导致先前的比较研究改善了BP性能，并且ABC方法的简单变化也提供了改进的ABC性能。随着两个改进的应用，我们得出结论，ABC方法确实对小问题进行了很好的表现，但实现的概括性表现仅仅比六个数据集中的一个单一的标准BP明显优于标准BP，并且训练时间随着问题的规模而迅速增加。生长。

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