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International Conference of Pioneering Computer Scientists, Engineers and Educators

ICPCSEE 2019: Data Science pp 433–444Cite as

Adaptive Flower Pollination Algorithm Based on Chaotic Map

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1059))

Abstract

Flower pollination algorithm (FPA) is one of the well-known evolutionary techniques used extensively to solve optimization problems. Despite its efficiency and wide use, the identical search behaviors may lead the algorithm to converge to local optima. In this paper, an adaptive FPA based on chaotic map (CAFPA) is proposed. The proposed algorithm first used the ergodicity of the logistic chaos mechanism, and chaotic mapping of the initial population to make the initial iterative population more evenly distributed in the solution space. Then at the self-pollination stage, the over-random condition of the gamete renewal was improved, the traction force of contemporary optimal position was given, and adaptive logarithmic inertia weight was introduced to adjust the proportion between the contemporary pollen position and disturbance to improve the performance of the algorithm. By comparing the new algorithm with three famous optimization algorithms, the accuracy and performance of the proposed approach are evaluated by 14 well-known benchmark functions. Statistical comparisons of experimental results show that CAFPA is superior to FPA, PSO, and BOA in terms of convergence speed and robustness.

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Acknowledgments

This study is supported by the National Natural Science Foundation of China (No. 71601071), the Science & Technology Program of Henan Province, China (No. 182102310886 and 162102110109), and an MOE Youth Foundation Project of Humanities and Social Sciences (No. 15YJC630079). We are particularly grateful to the suggestions of the editor and the anonymous reviewers which is greatly improved the quality of the paper.

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Authors and Affiliations

  1. Institute of Management Science and Engineering, Henan University, Kaifeng, 475004, China

    Yu Li

  2. Business School, Henan University, Kaifeng, 475004, China

    Yu Li, Juan Zheng & Yi-ran Zhao

Authors
  1. Yu Li
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  2. Juan Zheng
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  3. Yi-ran Zhao
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Corresponding author

Correspondence to Juan Zheng .

Editor information

Editors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Rui Mao

  2. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  3. Guilin University of Technology, Guilin, China

    Xiaolan Xie

  4. National Academy of Guo Ding Institute of Data Science, Harbin, China

    Zeguang Lu

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Cite this paper

Li, Y., Zheng, J., Zhao, Yr. (2019). Adaptive Flower Pollination Algorithm Based on Chaotic Map. In: Mao, R., Wang, H., Xie, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0121-0_34

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  • DOI: https://doi.org/10.1007/978-981-15-0121-0_34

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0120-3

  • Online ISBN: 978-981-15-0121-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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