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Phasor Symbiotic Organisms Search Algorithm for Global Optimization

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Intelligent Computing Theories and Application (ICIC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12463))

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Abstract

The symbiotic organisms search algorithm is a meta-heuristic algorithm based on the establishment of symbiotic relationships between populations in recent years. In this study, a novel phasor symbiotic organisms search algorithm (PSOS) based on phasor theory is proposed. Both the phase angle and the trigonometric function in the proposed PSOS are used to set parameters that can have the same or different directions, similar or diverse values. Different combinations of these parameters can better control the local exploitation and global exploration of PSOS. This makes the PSOS algorithm have stronger global optimization ability, avoiding premature algorithm or falling into local optimization. In order to verify the effectiveness of the algorithm, this study selected 23 benchmark test functions and a classic engineering optimization problem for testing. The results show that the PSOS algorithm has higher convergence accuracy and stronger robustness.

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Acknowledgement

This work is supported by the Funds for National Natural Science Foundation of China (grant number 61871040); the Key Program of National Natural Science Foundation of China (grant number 61731003) and the Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education.

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Authors and Affiliations

  1. School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China

    Fahui Miao, Li Yao, Xiaojie Zhao & Yawen Zheng

Authors
  1. Fahui Miao
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  2. Li Yao
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  3. Xiaojie Zhao
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  4. Yawen Zheng
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Corresponding author

Correspondence to Xiaojie Zhao .

Editor information

Editors and Affiliations

  1. Institute of Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. Electrical and Electronics Department, Polytechnic University of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Miao, F., Yao, L., Zhao, X., Zheng, Y. (2020). Phasor Symbiotic Organisms Search Algorithm for Global Optimization. In: Huang, DS., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12463. Springer, Cham. https://doi.org/10.1007/978-3-030-60799-9_6

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  • DOI: https://doi.org/10.1007/978-3-030-60799-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60798-2

  • Online ISBN: 978-3-030-60799-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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