A New Advantage Sharing Inspired Particle Swarm Optimization Algorithm

  • Lingping Kong
  • Václav SnášelEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 834)


Particle swarm optimization algorithm is a widely used computational method for optimizing a problem. This algorithm has been applied to many applications due to its easy implementation and few particles required. However, there is a big problem with the PSO algorithm, all the virtual particles converged to a point which may or may not be the optimum. In the paper, we propose an improved version of PSO by introducing the idea of advantage sharing and pre-learning walk mode. The advantage sharing means that the good particles share their advantage attributes to the evolving ones. The pre-learning walk mode notices one particle if it should continue to move or not which uses the feedback of the last movement. Two more algorithms are simulated as the comparison methods to test Benchmark function. The experimental results show that our proposed scheme can converge to a better optimum than the comparison algorithms.


Particle swarm optimization Advantage sharing Benchmark function 


  1. 1.
    Derrac, J., Salvador, G., Daniel, M., Francisco, H.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut. Comput. 1(1), 3–18 (2011)CrossRefGoogle Scholar
  2. 2.
    Shu-Chuan, C., Pei-Wei, T., Jeng-Shyang, P.: Cat swarm optimization. In: Pacific Rim International Conference on Artificial Intelligence. Springer, Berlin, Heidelberg (2006)Google Scholar
  3. 3.
    Thi-Kien, D., Tien-Szu, P., Trong-The, N., Shu-Chuan, C.: A compact articial bee colony optimization for topology control scheme in wireless sensor networks. J. Inf. Hiding Multimed. Signal Process. 6(2), 297–310 (2015)Google Scholar
  4. 4.
    Wuling, R., Cuiwen, Z.: A localization algorithm based On SFLA and PSO for wireless sensor network. Inf. Technol. J. 12(3), 502–505 (2013)CrossRefGoogle Scholar
  5. 5.
    Kaur, P., Shikha, M.: Resource provisioning and work flow scheduling in clouds using augmented Shuffled Frog Leaping Algorithm. J. Parallel Distrib. Comput. 101, 41–50 (2017)CrossRefGoogle Scholar
  6. 6.
    Ishaque, K., Zainal, S., Muhammad, A., Saad, M.: An improved particle swarm optimization (PSO) based MPPT for PV with reduced steady-state oscillation. IEEE Trans. Power Electron. 27(8), 3627–3638 (2012)CrossRefGoogle Scholar
  7. 7.
    Kennedy, J.: Particle swarm optimization. In: Encyclopedia of machine learning, pp. 760–766. Springer, Boston, MA (2011)Google Scholar
  8. 8.
    Marinakis, Y., Magdalene, M.: A hybrid genetic particle swarm optimization algorithm for the vehicle routing problem. Expert Syst. Appl. 37(2), 1446–1455 (2010)CrossRefGoogle Scholar
  9. 9.
    Nickabadi, A., Mohammad, M.E., Reza, S.: A novel particle swarm optimization algorithm with adaptive inertia weight. Appl. Soft Comput. 11(4), 3658–3670 (2011)CrossRefGoogle Scholar
  10. 10.
    Moradi, M.H., Abedini, M.: A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Int. J. Electrical Power Energy Syst. 34(1), 66–74 (2012)CrossRefGoogle Scholar
  11. 11.
    Niknam, T.: A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem. Appl. Energy 87(1), 327–339 (2010)CrossRefGoogle Scholar
  12. 12.
    Cheng, R., Yaochu, J.: A social learning particle swarm optimization algorithm for scalable optimization. Inf. Sci. 291, 43–60 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstravaCzech Republic

Personalised recommendations