Using spotted hyena optimizer for training feedforward neural networks. (January 2021)

• Record Type: Journal Article
• Authors: Luo, Qifang; Li, Jie; Zhou, Yongquan; Liao, Ling
• Is Part Of: Cognitive systems research. Volume 65(2021)
• Page Start: 1
• Page End: 16
• Publication Date: 2021-01
• Subjects: Spotted hyena optimizer -- Feedforward neural networks -- Classification datasets -- Metaheuristic optimization; Cognition -- Periodicals; Cognitive engineering (System design) -- Periodicals; Artificial intelligence -- Periodicals; 153.05
• DOI: 10.1016/j.cogsys.2020.09.001
• Languages: English
• ISSNs: 1389-0417

Abstract

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.