A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM

Wenbiao Yang, Kewen Xia, Tiejun Li, Min Xie and Fei Song
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Wenbiao Yang: School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China
Kewen Xia: School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China
Tiejun Li: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Min Xie: School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China
Fei Song: School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China

Mathematics, 2021, vol. 9, issue 3, 1-34

Abstract: A novel semi-supervised learning method is proposed to better utilize labeled and unlabeled samples to improve classification performance. However, there is exists the limitation that Laplace regularization in a semi-supervised extreme learning machine (SSELM) tends to lead to poor generalization ability and it ignores the role of labeled information. To solve the above problems, a Joint Regularized Semi-Supervised Extreme Learning Machine (JRSSELM) is proposed, which uses Hessian regularization instead of Laplace regularization and adds supervised information regularization. In order to solve the problem of slow convergence speed and the easy to fall into local optimum of marine predator algorithm (MPA), a multi-strategy marine predator algorithm (MSMPA) is proposed, which first uses a chaotic opposition learning strategy to generate high-quality initial population, then uses adaptive inertia weights and adaptive step control factor to improve the exploration, utilization, and convergence speed, and then uses neighborhood dimensional learning strategy to maintain population diversity. The parameters in JRSSELM are then optimized using MSMPA. The MSMPA-JRSSELM is applied to logging oil formation identification. The experimental results show that MSMPA shows obvious superiority and strong competitiveness in terms of convergence accuracy and convergence speed. Also, the classification performance of MSMPA-JRSSELM is better than other classification methods, and the practical application is remarkable.

Keywords: marine predator algorithm; learning strategy; semi-supervised extreme learning machine; oil layer identification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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