ABSTRACT

Nowadays, artificial intelligence has gained recognition in every aspect of life. Artificial neural networks, one of the most efficient artificial intelligence techniques, is remarkably successful in computers’ acquisition of the learning and interpretation capabilities of humans and attainment of meaningful results. Whether artificial intelligence networks can yield meaningful results is directly related to how the network is trained. The traditional algorithms, which are used to train artificial intelligence networks, do not always yield successful results in complicated problems and real-life problems. Metaheuristic algorithms are efficient techniques developed in order to figure out time-consuming and challenging problems fast and as optimally as possible. This study makes use of the artificial bee colony algorithm, which has been widely used recently in solving many kinds of problems so as to train artificial neural networks efficiently. Within this study, different strategies are used on subpopulations, so that the algorithm can search without getting tangled with the local optima. And also same and different parameter settings are considered for each population to reflect different search behaviours. The proposed approach was analysed through applied results of different data sets. The results yielded that the used strategies can be promising alternatives to the current search algorithms.

摘要

如今，人工智能在生活的方方面面都得到了认可。人工神经网络是最有效的人工智能技术之一，在计算机获取人类的学习和解释能力并获得有意义的结果方面非常成功。人工智能网络能否产生有意义的结果与网络的训练方式直接相关。用于训练人工智能网络的传统算法在复杂问题和现实问题中并不总是能产生成功的结果。元启发式算法是一种有效的技术，旨在快速、尽可能优化地解决耗时且具有挑战性的问题。本研究利用人工蜂群算法，它最近被广泛用于解决多种问题，从而有效地训练人工神经网络。在这项研究中，对子种群使用了不同的策略，因此算法可以在不与局部最优值纠缠的情况下进行搜索。并且为每个群体考虑相同和不同的参数设置以反映不同的搜索行为。通过不同数据集的应用结果分析了所提出的方法。结果表明，所使用的策略可以是当前搜索算法的有希望的替代方案。并且为每个群体考虑相同和不同的参数设置以反映不同的搜索行为。通过不同数据集的应用结果分析了所提出的方法。结果表明，所使用的策略可以是当前搜索算法的有希望的替代方案。并且为每个群体考虑相同和不同的参数设置以反映不同的搜索行为。通过不同数据集的应用结果分析了所提出的方法。结果表明，所使用的策略可以是当前搜索算法的有希望的替代方案。