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Simplified Particle Swarm Optimization Algorithm Based on Improved Learning Factors

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Advances in Neural Networks - ISNN 2017 (ISNN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10261))

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Abstract

To overcome the shortcomings of the traditional particle swarm optimization algorithm, which are easy to fall into local extreme, a new algorithm based on the simplified particle swarm optimization algorithm is proposed. Firstly, the proposed algorithm removes the speed term, so that it makes the algorithm simple. And then it improves the displacement term. Finally the nonlinearity of the trigonometric function is utilized in the algorithm to improve learning factors. It balances the global search and local search. The six basic test functions are used to compare the standard particle swarm optimization algorithm, the simplified particle swarm optimization algorithm and the improved algorithm proposed in this paper. Experimental results show that the performance of the improved particle swarm optimization is better than the other two algorithms.

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References

  1. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)

    Article  Google Scholar 

  2. Setayesh, M., Zhang, M., Johnston, M.: A novel particle swarm optimization approach to detecting continuous, thin and smooth edges in noisy images. Inf. Sci. 246, 28–51 (2013)

    Article  MATH  Google Scholar 

  3. Calazan, R.M., Nedjah, N., Mourelle, L.M.: A hardware accelerator for particle swarm optimization. Appl. Soft Comput. 14, 347–356 (2014)

    Article  Google Scholar 

  4. Echevarr, L.C., Santiago, O.L., Fajardo, J.A.H., Neto, A.J.S., Sanchez, D.J.: A variant of the particle swarm optimization for the improvement of fault diagnosis in industrial systems via faults estimation. Eng. Appl. Artif. Intell. 212, 1–16 (2014)

    Article  Google Scholar 

  5. Sun, Z., Zhao, J., Wang, W.: Application of improved particle swarm optimization based on gaussian search to grinding predictive control. J. Dalian Univ. Technol. 1, 89–96 (2015)

    Google Scholar 

  6. Li, N., Huang, Z.: Improved niche particle swarm optimization. Softw. Guide 2, 45–47 (2015)

    Google Scholar 

  7. Meng, Q., Zhang, M., Yang, G., Qiu, R., Ming, X.: Guidance line recognition of agricultural machinery based on particle swarm optimization under natural illumination. J. Agric. Mach. 47, 11–20 (2016)

    Google Scholar 

  8. Ratnaweera, A., Halgamuge, S.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. Evol. Comput. 8, 240–255 (2004)

    Article  Google Scholar 

  9. Zhao, Z., Huang, S., Wang, W.: Simplified particle swarm optimization algorithm based on stochastic inertia weight. Comput. Appl. Res. 22, 361–363 (2014)

    Google Scholar 

  10. Chen, S., Cai, G., Guo, W., Chen, G.: Study on nonlinear strategy of acceleration coefficient in particle swarm optimization (PSO) algorithm. J. Yangtze Univ. (Nat. Sci. Ed.) 4, 1–4 (2007)

    Google Scholar 

  11. Zhang, S., Zhong, W.: Hybrid particle swarm optimization algorithm of new learning factors and constraint factor. Appl. Res. Comput. 32, 3626–3628 (2015)

    Google Scholar 

  12. Clerc, M.: The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Proceeding of IEEE International Conference on Evolutionary Computation, pp. 1951–1957 (1999)

    Google Scholar 

  13. Zhu, X., Li, Y., Li, N., Fan, B.: Improved PSO algorithm based on swarm prematurely degree and nonlinear periodic oscillating strategy. J. Commun. 35, 182–189 (2014)

    Google Scholar 

  14. Zhang, L., Wu, Y., Wei, X.: An adaptive particle swarm optimization algorithm based on linear dynamic parameter. Res. Dev. 3, 15–18 (2011)

    Google Scholar 

  15. Wang, Q., Wang, Z., Wang, S.: A modified particle swarm optimizer using dynamic inertia weight. Chin. Mech. Eng. 16, 945–948 (2005)

    Google Scholar 

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Acknowledgement

This work was supported by The National Natural Science Foundation of China (Project No. 61373067, 61662057).

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Authors and Affiliations

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

    Wei Gao & Chuyi Song

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

    **gqing Jiang & Chenggang Zhang

  3. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, People’s Republic of China

    **gqing Jiang

Authors
  1. Wei Gao
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  2. Chuyi Song
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  3. **gqing Jiang
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  4. Chenggang Zhang
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Corresponding author

Correspondence to **gqing Jiang .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Bei**g, China

    Qinglai Wei

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Gao, W., Song, C., Jiang, J., Zhang, C. (2017). Simplified Particle Swarm Optimization Algorithm Based on Improved Learning Factors. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_38

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  • DOI: https://doi.org/10.1007/978-3-319-59072-1_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59071-4

  • Online ISBN: 978-3-319-59072-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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