title: Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm.
abstract: 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.
authors.affiliation: College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China.
authors.forename: Haizhou
authors.initials: H
authors.lastname: Wu
authors.affiliation: 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.
authors.forename: Yongquan
authors.identifier: 0000-0003-4404-952X
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: 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.
authors.forename: Qifang
authors.initials: Q
authors.lastname: Luo
authors.affiliation: Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt.
authors.forename: Mohamed Abdel
authors.identifier: 0000-0002-5572-0721
authors.initials: MA
authors.lastname: Basset
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D003198:Computer Simulation; D057225:Data Mining; D005246:Feedback; D006801:Humans; D007858:Learning; D016571:Neural Networks, Computer; D013561:Symbolism