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Elitist teaching–learning-based optimization (ETLBO) with higher-order Jordan Pi-sigma neural network: a comparative performance analysis

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Abstract

This paper presents the performance analysis of a newly developed elitist teaching–learning-based optimization algorithm applied with an efficient higher-order Jordan Pi-sigma neural network (JPSNN) for real-world data classification. Teaching–learning-based optimization (TLBO) algorithm is a recent metaheuristic, which is inspired through the teaching and learning process of both teacher and learner. As compared to other algorithms, it is efficient and robust due to its non-controlling parameter adjustments feature. Elitist TLBO is an improved version of TLBO with the addition of elitist solutions, which makes it more efficient. During the experiment, first the TLBO and then ETLBO algorithm are applied with only Pi-sigma neural network and its performance has been compared with other methods such as GA and PSO. Then, the ETLBO algorithm is applied with JPSNN and found better results over other methods. The proposed method has been tested with real-world benchmark datasets considered from UCI machine learning repository, and the performance has been compared with all seven approaches along with other HONN to prove the effectiveness of the method. Simulation results and statistical analysis show the superiority in the performance of the proposed approach as well as prove the potentiality over other existing approaches.

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Acknowledgements

This work is supported by Department of Science and Technology (DST), Ministry of Science and Technology, New Delhi, Government of India, under Grants No. DST/INSPIRE Fellowship/2013/585. The author would like to thank to the editor and the reviewers for their valuable comments and suggestions that helped to improve the content of the paper in a large extent.

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Authors and Affiliations

  1. Department of Computer Science Engineering, Modern Engineering and Management Studies, Balasore, Odisha, India

    Janmenjoy Nayak

  2. Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Sambalpur, 768018, Odisha, India

    Bighnaraj Naik

  3. Department of Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, 768018, India

    H. S. Behera

  4. Machine Intelligence Research Labs (MIR Labs), 2259, Auburn, WA, 98071-2259, USA

    Ajith Abraham

  5. IT4Innovations - Center of Excellence, VSB-Technical University of Ostrava, 17. listopadu 2172/15, 708 00, Ostrava, Czech Republic

    Ajith Abraham

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Nayak, J., Naik, B., Behera, H.S. et al. Elitist teaching–learning-based optimization (ETLBO) with higher-order Jordan Pi-sigma neural network: a comparative performance analysis. Neural Comput & Applic 30, 1445–1468 (2018). https://doi.org/10.1007/s00521-016-2738-1

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