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A Modified Teaching-Learning Optimization Algorithm for Economic Load Dispatch Problem

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Intelligent Computing Methodologies (ICIC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10956))

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Abstract

For the original Teaching-learning algorithm, it is weak in global search and prone to local search when solving complex optimization problems of high dimension. A modified algorithm based on space reverse-solution is proposed in this paper. Improvement of teacher phrase is based on the chaotic mapping and that of student phrase is based on the multi learning strategy. Then Self-learning phrase is added. The modified algorithm is applied to the complex high-dimensional benchmark functions for simulation experiments. Finally, the modified algorithm is applied to two typical power load distribution problems including 13 units and 40 units. The validity of the algorithm is verified from the aspects of convergence speed, convergence accuracy and stability.

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Acknowledgement

This work was supported by National Key R&D Program of China (2017YFF0108800) and the National Natural Science Foundation of China (61473069, 61627809).

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110004, China

    Ge Yu & Jinhai Liu

Authors
  1. Ge Yu
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  2. Jinhai Liu
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Corresponding author

Correspondence to Ge Yu .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Indian Institute of Technology Madras, Chennai, India

    M. Michael Gromiha

  3. Inha University, Incheon, Korea (Republic of)

    Kyungsook Han

  4. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

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Yu, G., Liu, J. (2018). A Modified Teaching-Learning Optimization Algorithm for Economic Load Dispatch Problem. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-95957-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95956-6

  • Online ISBN: 978-3-319-95957-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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