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首页> 外文会议>International Conference on Latest Trends in Electrical Engineering and Computing Technologies >Quick Guided Artificial Bee Colony (QGABC) algorithm for training neural networks on classification and prediction tasks
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Quick Guided Artificial Bee Colony (QGABC) algorithm for training neural networks on classification and prediction tasks

机译：快速引导人工蜂殖民地（QGABC）培训神经网络对分类和预测任务的算法

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摘要

The super-metaheurtic are among the most attractive optimization algorithms based on the nature inspired social insects acting like ants, birds and bees. Artificial Bee Colony (ABC) is one of them, which uses the environment behaviors of honey bees for solving different linear and nonlinear complex problems. ABC algorithm has been used for solving a couple of optimization problems efficiently; however, employed and onlooker bees similar actions of inspiring trapped it in local minima and slow convergences speed as well. To increase the performance of a typical ABC with respect to exploration and exploitation process, Guided ABC and Quick ABC algorithms are hybrid named Quick Guided Artificial Bee Colony (QGABC) algorithm. The proposed algorithm has been simulated on Boolean classification and clustering problems through the Neural Networks (NNs) training and testing process. From the simulation results, the QGABC algorithm has outperformed in comparison with the typical ABC, GGABC and QABC algorithms.
机译：超级元训练是基于自然的最具吸引力的优化算法之一，其自然启发了像蚂蚁，鸟类和蜜蜂的社会昆虫。人造蜜蜂殖民地（ABC）是其中之一，它使用蜂蜜蜜蜂的环境行为来解决不同的线性和非线性复杂问题。 ABC算法已用于有效解决几个优化问题;然而，就业和旁观者蜂蜜蜂在局部最小值和慢速收敛速度下捕获它的类似行动。为了提高典型ABC关于勘探和开发过程的性能，引导ABC和快速ABC算法是具有快速引导人造蜜蜂菌落（QGABC）算法的混合动力。通过神经网络（NNS）培训和测试过程，已经在布尔分类和聚类问题上模拟了所提出的算法。根据仿真结果，与典型的ABC，GGABC和QABC算法相比，QGABC算法表现优于。

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