摘要翻译：
反向传播算法是优化前馈神经网络训练中应用最广泛和最流行的技术之一。自然启发的元启发式算法也为优化复杂问题提供了无导数解。人工蜂群算法是一种模仿蜜蜂在蜂群中觅食或寻找食物源的行为的自然启发的元启发式算法，该算法在几个应用中得到了改进的优化结果。本文提出的方法包括一种改进的基于人工蜂群算法的反向传播神经网络训练方法，用于快速提高混合神经网络学习方法的收敛速度。用基于遗传算法的反向传播方法对结果进行了分析，这是同类方法中的另一种杂交方法。在标准数据集上进行了分析，反映了所提方法在收敛速度和速率方面的效率。
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英文标题：
《Training a Feed-forward Neural Network with Artificial Bee Colony Based
  Backpropagation Method》
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作者：
Sudarshan Nandy, Partha Pratim Sarkar and Achintya Das
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feed forward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-free solution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristic algorithm, mimicking the foraging or food source searching behaviour of bees in a bee colony and this algorithm is implemented in several applications for an improved optimized outcome. The proposed method in this paper includes an improved artificial bee colony algorithm based back-propagation neural network training method for fast and improved convergence rate of the hybrid neural network learning method. The result is analysed with the genetic algorithm based back-propagation method, and it is another hybridized procedure of its kind. Analysis is performed over standard data sets, reflecting the light of efficiency of proposed method in terms of convergence speed and rate.
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PDF链接：
https://arxiv.org/pdf/1209.2548