Using spotted hyena optimizer for training feedforward neural networks |
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Affiliation: | 1. Department of Science and Technology Teaching, China University of Political Science and Law, Beijing 100088, China;2. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China;3. Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence, Nanning 530006, China |
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Abstract: | Spotted hyena optimizer (SHO) is a novel metaheuristic optimization algorithm based on the behavior of spotted hyena and their collaborative behavior in nature. In this paper, we design a spotted hyena optimizer for training feedforward neural network (FNN), which is regarded as a challenging task since it is easy to fall into local optima. Our objective is to apply metaheuristic optimization algorithm to tackle this problem better than the mathematical and deterministic methods. In order to confirm that using SHO to train FNN is more effective, five classification datasets and three function-approximations are applied to benchmark the performance of the proposed method. The experimental results show that the proposed SHO algorithm for optimization FNN has the best comprehensive performance and has more outstanding performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures. |
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Keywords: | Spotted hyena optimizer Feedforward neural networks Classification datasets Metaheuristic optimization |
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