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Convergence Analysis of Self-adaptive Immune Particle Swarm Optimization Algorithm

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Advances in Neural Networks – ISNN 2018 (ISNN 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10878))

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Abstract

The self-adaptive immune particle swarm optimization (SAIPSO) algorithm is a hybrid algorithm based on immune algorithm and particle swarm optimization algorithm. SAIPSO algorithm has been implemented and achieved better result compared with the classical particle swarm optimization algorithm. However, the theoretical support of the algorithm is equally important as the implementation of the algorithm. Therefore, this paper mainly uses the convergence theorem of random search algorithm and the mathematical induction to prove the convergence of SAIPSO algorithm, which will help the improvement and application of the algorithm in the future.

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Acknowledgement

This work was supported by The National Natural Science Foundation of China (Project No. 61662057, 61672301) and Higher Educational Scientific Research Projects of Inner Mongolia Autonomous Region (Project No. NJZC17198).

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Authors and Affiliations

  1. College of Computer Science and Technology, Inner Mongolia University for Nationalities, Tongliao, 028043, China

    Jingqing Jiang & Chenggang Zhang

  2. College of Mathematics, Inner Mongolia University for Nationalities, Tongliao, 028043, China

    Jingqing Jiang, Chuyi Song & Huan Ping

Authors
  1. Jingqing Jiang
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  2. Chuyi Song
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  3. Huan Ping
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  4. Chenggang Zhang
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Corresponding author

Correspondence to Huan Ping .

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Editors and Affiliations

  1. Texas A&M University at Qatar, Doha, Qatar

    Tingwen Huang

  2. Sichuan University, Chengdu, China

    Jiancheng Lv

  3. Southeast University, Nanjing, China

    Changyin Sun

  4. United Institute of Informatics Problems, Minsk, Belarus

    Alexander V. Tuzikov

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Jiang, J., Song, C., Ping, H., Zhang, C. (2018). Convergence Analysis of Self-adaptive Immune Particle Swarm Optimization Algorithm. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_19

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  • DOI: https://doi.org/10.1007/978-3-319-92537-0_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92536-3

  • Online ISBN: 978-3-319-92537-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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