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A Modified Particle Swarm Optimizer for Tracking Dynamic Systems

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Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 3612))

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Abstract

The paper proposes a modified particle swarm optimizer for tracking dynamic systems. In the new algorithm, the changed local optimum and global optimum are introduced to guide the movement of each particle and avoid making direction and velocity decisions on the basis of the outdated information. An environment influence factor is put forward based on the two optimums above, which dynamically decide the change of the inertia weight. The combinations of the different local optimum update strategy and local inertia weight update strategy are tested on the parabolic benchmark function. The results on the benchmark function with various severities suggest that modified particle swarm optimizer performs better in convergence speed and aggregation accuracy.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Xi’an Jiaotong University, Xi’an, P.R.C

    Xuanping Zhang, Yuping Du, Zheng Qin, Guoqiang Qin & Jiang Lu

Authors
  1. Xuanping Zhang

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  2. Yuping Du

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  3. Zheng Qin

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  4. Guoqiang Qin

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  5. Jiang Lu

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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Zhang, X., Du, Y., Qin, Z., Qin, G., Lu, J. (2005). A Modified Particle Swarm Optimizer for Tracking Dynamic Systems. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_72

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