College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China.
College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China; Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence, Nanning 530006, China.
Comput Intell Neurosci. 2016;2016:9063065. doi: 10.1155/2016/9063065. Epub 2016 Dec 25.
Symbiotic organisms search (SOS) is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs). In this paper, SOS is employed as a new method for training FNNs. To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms. The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed. It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared.
共生生物体搜索（SOS）是一种新的强大的元启发式算法，它激发了生物体在生态系统中生存和繁殖所采用的共生相互作用策略。在监督学习领域，为前馈神经网络（FNN）提供令人满意和高效的训练算法是一项具有挑战性的任务。在本文中，SOS 被用作训练 FNN 的新方法。为了研究上述方法的性能，从 UCI 机器学习存储库中选择了八个不同的数据集进行实验，并在七种元启发式算法之间进行了比较。结果表明，SOS 在收敛速度方面优于其他算法，用于训练 FNN。还证明了通过 SOS 方法训练的 FNN 的准确性优于大多数比较算法。
