Improved Directionally Driven Self-regulating Particle Swarm Optimizer

  • Saumya Jariwala
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 801)


Particle Swarm Optimization (PSO) and many of its variants tend to suffer from premature convergence on strongly multimodal test problems. It is known that maintenance of diversity in the swarm is very important for preventing the swarm from converging prematurely for multimodal function problems. In this paper, an improved version of the Directionally Driven Self-Regulating Particle Swarm Optimization (DD-SRPSO) algorithm is proposed with a mechanism to maintain diversity without incurring any significant computational cost referred to as Improved DD-SRPSO. An attractive and repulsive swarm update strategy inspired from ARPSO is incorporated in DD-SRPSO. The proposed method is tested using the shifted, rotated and complex benchmark functions from CEC2013. These results are compared with eight PSO variants including DD-SRPSO. The results indicate that the proposed method improves results on some of the benchmark functions and gives comparable results for others.


Particle swarm optimization Directional update strategy Rotational invariant strategy Diversity guided search 



The author thanks Prof. Suresh Sundaram and Dr. Senthilnath J. for the help and guidance provided by them.


  1. 1.
    Wari, E., Zhu, W.: A survey on metaheuristics for optimization in food manufacturing industry. Appl. Soft Comput. 46, 328–343 (2016)CrossRefGoogle Scholar
  2. 2.
    Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of machine learning, pp. 760–766. Springer, Heidelberg (2011). Scholar
  3. 3.
    Cheng, R., Jin, Y.: A social learning particle swarm optimization algorithm for scalable optimization. Inf. Sci. 291, 43–60 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Evers, G.I., Ghalia, M.B.: Regrouping particle swarm optimization: a new global optimization algorithm with improved performance consistency across benchmarks. In: IEEE International Conference on Systems, Man and Cybernetics. SMC 2009, pp. 3901–3908. IEEE (2009)Google Scholar
  5. 5.
    Peram, T., Veeramachaneni, K., Mohan, C.K.: Fitness-distance-ratio based particle swarm optimization. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS 2003, pp. 174–181. IEEE (2003)Google Scholar
  6. 6.
    Xinchao, Z.: A perturbed particle swarm algorithm for numerical optimization. Appl. Soft Comput. 10(1), 119–124 (2010)CrossRefGoogle Scholar
  7. 7.
    Riget, J., Vesterstrøm, J.S.: A diversity-guided particle swarm optimizer-the ARPSO. Department of Computer Science, University of Aarhus, Aarhus, Denmark. Technical report 2 (2002)Google Scholar
  8. 8.
    Krink, T., VesterstrOm, J.S., Riget, J.: Particle swarm optimisation with spatial particle extension. In: Proceedings of the 2002 Congress on Evolutionary Computation. CEC 2002, vol. 2, pp. 1474–1479. IEEE (2002)Google Scholar
  9. 9.
    Wang, H., Li, H., Liu, Y., Li, C., Zeng, S.: Opposition-based particle swarm algorithm with Cauchy mutation. In: IEEE Congress on Evolutionary Computation. CEC 2007, pp. 4750–4756. IEEE (2007)Google Scholar
  10. 10.
    Van Den Bergh, F.: An analysis of particle swarm optimizers. Ph.D. thesis, University of Pretoria (2007)Google Scholar
  11. 11.
    Tanweer, M.R., Auditya, R., Suresh, S., Sundararajan, N., Srikanth, N.: Directionally driven self-regulating particle swarm optimization algorithm. Swarm Evol. Comput. 28, 98–116 (2016)CrossRefGoogle Scholar
  12. 12.
    Liang, J., Qu, B., Suganthan, P., Hernández-Díaz, A.G.: Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore. Technical report 201212, 3–18 (2013)Google Scholar
  13. 13.
    Tanweer, M.R., Suresh, S., Sundararajan, N.: Self regulating particle swarm optimization algorithm. Inf. Sci. 294, 182–202 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Liang, J.J., Suganthan, P.N.: Dynamic multi-swarm particle swarm optimizer with local search. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 1, pp. 522–528. IEEE (2005)Google Scholar
  15. 15.
    Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of the 2002 Congress on Evolutionary Computation. CEC 2002, vol. 2, pp. 1671–1676. IEEE (2002)Google Scholar
  16. 16.
    Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation. CEC 1999, vol. 3, pp. 1945–1950. IEEE (1999)Google Scholar
  17. 17.
    Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)CrossRefGoogle Scholar
  18. 18.
    Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8(3), 204–210 (2004)CrossRefGoogle Scholar
  19. 19.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7(Jan), 1–30 (2006)MathSciNetzbMATHGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Dhirubhai Ambani Institute of Information and Communication TechnologyGandhinagarIndia

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