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International Conference on Intelligent Computing

ICIC 2019: Intelligent Computing Theories and Application pp 719–728Cite as

A Complex-Valued Encoding Moth-Flame Optimization Algorithm for Global Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11643))

Abstract

The real-valued moth-flame optimization algorithm (MFO) is a new bio-inspired algorithm. It simulates the navigation mechanism of moth lateral positioning under moonlight. MFO has excellent performance in solving optimization problems and has strong ability in solving power optimization combination. In order to improve the global search ability of the algorithm, a complex-valued encoding moth-flame optimization algorithm (CMFO) is proposed. The real and imaginary parts of the population are updated by using the diploid structure of complex-valued encoding. The diversity of the population was increased. The effectiveness of CMFO algorithm has been verified by 4 benchmark problems. Statistically significant results and analysis show that the proposed complex-valued encoding moth-flame optimization algorithm is very promising and occasionally competitive compared with other well-established meta-heuristic techniques.

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Acknowledgments

This work is supported by National Science Foundation of China under Grant No. 61563008. Project of Guangxi University for Nationalities Science Foundation under Grant No. 2018GXNSFAA138146.

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Authors and Affiliations

  1. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, 530006, China

    Pengchuan Wang, Yongquan Zhou, Qifang Luo, Chencheng Fan & Zhehong Xiang

  2. Guangxi High School Key Laboratory of Complex System and Computational Intelligence, Nanning, 530006, China

    Yongquan Zhou & Qifang Luo

Authors
  1. Pengchuan Wang
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  2. Yongquan Zhou
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  3. Qifang Luo
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  4. Chencheng Fan
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  5. Zhehong Xiang
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Corresponding author

Correspondence to Yongquan Zhou .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytechnic University of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

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Wang, P., Zhou, Y., Luo, Q., Fan, C., Xiang, Z. (2019). A Complex-Valued Encoding Moth-Flame Optimization Algorithm for Global Optimization. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_69

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  • DOI: https://doi.org/10.1007/978-3-030-26763-6_69

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26762-9

  • Online ISBN: 978-3-030-26763-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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