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Particle Swarm Optimization Based on the Winner’s Strategy

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Book cover Swarm, Evolutionary, and Memetic Computing (SEMCCO 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9873))

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Abstract

This paper presents particle swarm optimization based on winner’s strategy (PSO-WS). Instead of considering gbest and pbest particle for position update, each particle considers its distance from immediate winner to update its position. If this strategy performs well for the particle, then that particle updates its position based on this strategy, otherwise its position is replaced by its immediate winner particle’s position. Dimension dependant swarm size is used for better exploration. Proposed method is compared with CSO and CCPSO2, which are available to solve large scale optimization problems. Statistical results show that proposed method performs well for separable as well as non separable problems.

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References

  1. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: IEEE International Conference on Neural Network, Perth, Australia 1995, pp. 1942–1948 (1995)

    Google Scholar 

  2. Li, X., Deb, K.: Comparing lbest PSO niching algorithms using different position update rules. In: WCCI 2010 IEEE World Congress on Computational Intelligence 18–23 July, 2010 - CCIB, Barcelona, Spain, pp. 1564–1571 (2010)

    Google Scholar 

  3. Qu, B.Y., Suganthan, P.N., Das, Swagatam: A distance-based locally informed particle swarm model for multi-modal optimization. IEEE Trans. Evol. Comput. 17(3), 387–402 (2013)

    Article  Google Scholar 

  4. Wang, H., Moon, I., Yang, S., Wang, D.: A memetic particle swarm optimization algorithm for multimodal optimization problems. Inf. Sci. 197, 38–52 (2012)

    Article  Google Scholar 

  5. Zhan, Z.H., Zhang, J., Li, Y.: Adaptive particle swarm optimization. IEEE Trans. Syst. Man Cybern. Part B Cybern. 39(6), 1362–1381 (2009)

    Article  Google Scholar 

  6. Kiranyaz, S., Ince, T., Yildirim, A., Gabbouj, M.: Fractional particle swarm optimization in multidimensional search space. IEEE Trans. Syst. Man Cybern. Part B Cybern. 40(2), 298–319 (2010)

    Article  Google Scholar 

  7. Mirjalili, S., Lewis, A., Sadiq, A.S.: Autonomous particles groups for particle swarm optimization. Arab. J Sci. Eng. 39, 4683–4697 (2014)

    Article  Google Scholar 

  8. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1998), Piscataway, pp. 69–73 (1998)

    Google Scholar 

  9. van den Bergh, F.: An analysis of particle swarm optimizers, Ph.D. dissertation, Department of Computer Science, University of Pretoria, Pretoria, South Africa (2002)

    Google Scholar 

  10. Wang, H., et al.: Opposition-based particle swarm algorithm with cauchy mutation. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 4750–4756 (2007)

    Google Scholar 

  11. Yang, S., Wang, M.: A quantum particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2004), pp. 320–324 (2004)

    Google Scholar 

  12. Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer and its adaptive variant. IEEE Trans. Syst. Man Cybern. Part B Cybern. 35(6), 1272–1282 (2005)

    Article  Google Scholar 

  13. 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, pp. 3901–3908 (2009)

    Google Scholar 

  14. van den Bergh, F., Engelbrecht, A.: A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 225–239 (2004)

    Article  Google Scholar 

  15. Yang, Z., Tang, K., Yao, X.: Large scale evolutionary optimization using cooperative coevolution. Inf. Sci. 178(15), 2986–2999 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  16. Cui, Z.; Zeng, J.; Yin, Y.: An improved PSO with time-varying accelerator coefficients. In: Eighth International Conference on Intelligent Systems Design and Applications, Kaohsiung, pp. 638–643 (2008)

    Google Scholar 

  17. Ziyu, T., Dingxue, Z.: A modified particle swarm optimization with an adaptive acceleration coefficients. In: Asia-Pacific Conference on Information Processing, Shenzhen, pp. 330–332 (2009)

    Google Scholar 

  18. Bao, G.Q., Mao, K.F.: Particle swarm optimization algorithm with asymmetric time varying acceleration coefficients. In: IEEE International Conference on Robotics and Biomimetics, Guilin, pp. 2134–2139 (2009)

    Google Scholar 

  19. Dai, Y., Liu, L., Li, Y.: An intelligent parameter selection method for particle swarm optimization algorithm. In: Fourth International Joint Conference on Computational Sciences and Optimization, pp. 960–964 (2011)

    Google Scholar 

  20. Clerc, M., Kennedy, J.: The particle swarm: explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)

    Article  Google Scholar 

  21. Ray, T., Yao, X.: A cooperative coevolutionary algorithm with correlation based adaptive variable partitioning. In: Proceedings of IEEE CEC, May 2009, pp. 983–999 (2009)

    Google Scholar 

  22. Zhao, S.Z., Liang, J.J., Suganthan, P.N., Tasgetiren, M.F.: Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization. In: Proceedings of IEEE CEC, June 2008, pp. 3845–3852 (2008)

    Google Scholar 

  23. Shen, X., Chi, Z., Yang, J., Chen, C.: Particle swarm optimization with dynamic adaptive inertia weight. In: International Conference on Challenges in Environmental Science and Engineering, pp 287–289 (2010)

    Google Scholar 

  24. Helwig, S., Branke, J., Mostaghim, S.: Experimental analysis of bound handling techniques in particle swarm optimization. In: IEEE (2011)

    Google Scholar 

  25. Omidvar, M.N., Li, X., Yao, X.: Cooperative co-evolution with delta grouping for large scale non-separable function optimization. In: WCCI 2010 IEEE World Congress on Computational Intelligence, 18–23 July, 2010 - CCIB, Barcelona, Spain, pp. 1762–1769 (2010)

    Google Scholar 

  26. Epitropakis, M.G., Plagianakos, V.P., Vrahatis, M.N.: Evolving cognitive and social experience in particle swarm optimization through differential evolution: a hybrid approach. Inf. Sci. 216, 50–92 (2012)

    Article  Google Scholar 

  27. Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans. Evol. Comput. 8(3), 240–255 (2004)

    Article  Google Scholar 

  28. Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proceedings of IEEE International Congress on Evolutionary Computation, vol. 3, pp. 101–106 (1999)

    Google Scholar 

  29. Bonyadi, M.R., Michalewicz, Z., Li, X.: An analysis of the velocity updating rule of the particle swarm optimization algorithm. J. Heuristics 20(4), 417–452 (2014)

    Article  Google Scholar 

  30. Li, X., Yao, X.: Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans. Evol. Comput. 16(2), 210–224 (2012)

    Article  MathSciNet  Google Scholar 

  31. Bonyadi, M.R., Michalewicz, Z.: A locally convergent rotationally invariant particle swarm optimization algorithm. Swarm Intell. 8(3), 159–198 (2014)

    Article  Google Scholar 

  32. Tang, K., Yáo, X., Suganthan, P.N., MacNish, C., Chen, Y.P., Chen, C.M., Yang, Z.: Benchmark functions for the CEC 2008 special session and competition on large scale global optimization, Nature Inspired Computation and Applications Laboratory, University of Science and Technology, Hefei, China, Technical report (2007). http://nical.ustc.edu.cn/cec08ss.php

  33. Cheng, Ran, Jin, Yaochu: A competitive swarm optimizer for large scale optimization. IEEE Trans. Cybern. 45(2), 191–204 (2015)

    Article  Google Scholar 

  34. Zhao, S.Z., Suganthan, P.N., Das, S.: Self-adaptive differential evolution with multi-trajectory search for large scale optimization. Soft. Comput. 15(11), 2175–2185 (2011). doi:10.1007/s00500-010-0645-4

    Article  Google Scholar 

  35. Zhao, S.Z., Liang, J.J., Suganthan, P.N., Tasgetiren, M.F.: Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization. In: IEEE Congress on Evolutionary Computation, pp. 3845–3852, Hong Kong, June 2008

    Google Scholar 

  36. Hao, Z., Guo, G., Huang, H.: A particle swarm optimization algorithm with differential evolution. In: 2007 International Conference on Machine Learning and Cybernetics, vol. 2, pp. 1031–1035 (2007)

    Google Scholar 

  37. Omran, M.G., Engelbrecht, A.P., Salman, A.: Differential evolution based particle swarm optimization. In: 2007 IEEE Swarm Intelligence Symposium, SIS 2007, pp. 112–119 (2007)

    Google Scholar 

  38. Kennedy, J.: Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: IEEE, pp. 1931–1938 (2009)

    Google Scholar 

  39. Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of the Congress on Evolutionary Computation, pp. 1671–1676 (2002)

    Google Scholar 

  40. Mendes, R.: Population topologies and their influence in particle swarm performance, Ph.D. dissertation, Escola de Engenharia, Universidade do Minho, Portugal (2004)

    Google Scholar 

  41. Emara, H.M.: Adaptive clubs-based particle swarm optimization. In: American Control Conference 2009, ACC 2009, pp. 5628–5634 (2009)

    Google Scholar 

  42. Elsayed, S.M., Sarker, R.A. and Essam, D.L.: Memetic multi-topology particle swarm optimizer for constrained optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1–8 (2012)

    Google Scholar 

  43. Gong, Y.J., Zhang, J.: Small-world particle swarm optimization with topology adaptation. In: Proceedings of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference, pp. 25–32 (2013)

    Google Scholar 

  44. Liang, S., Song, S., Kong, L. Cheng, J.: An improved particle swarm optimization algorithm and its convergence analysis. In: Second International Conference on Computer Modeling and Simulation, pp. 138–141 (2010)

    Google Scholar 

  45. Bird, S., Li, X.: Improving local convergence in particle swarms by fitness approximation using regression. In: Tenne, Y., Goh, C.K. (eds.) Computational Intelligence in Expensive Optimization Problems, pp. 265–293. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  46. Chen, W.N., Zhang, J., Lin, Y., Chen, N., Zhan, Z.H., Chung, H.S.H., Li, Y., Shi, Y.H.: Particle swarm optimization with an aging leader and challengers. IEEE Trans. Evol. Comput. 17(2), 241–258 (2013)

    Article  Google Scholar 

  47. Qu, B.Y., Suganthan, P.N., Das, S.: A distance-based locally informed particle swarm model for multi-modal optimization. IEEE Trans. Evol. Comput. 17(3), 387–402 (2013)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of CSE, RCOEM, Nagpur, India

    Shailendra S. Aote

  2. YCCE, Nagpur, India

    M. M. Raghuwanshi

  3. CSE, GHRCE, Nagpur, India

    L. G. Malik

Authors
  1. Shailendra S. Aote
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  2. M. M. Raghuwanshi
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  3. L. G. Malik
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Corresponding author

Correspondence to M. M. Raghuwanshi .

Editor information

Editors and Affiliations

  1. IIT, New Dehli, India

    Bijaya Ketan Panigrahi

  2. Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Indian Statistical Institute, Kolkata, India

    Swagatam Das

  4. Department of Computer Science Engineering, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, India

    Suresh Chandra Satapathy

Appendix

Appendix

Seven benchmark functions are used in CEC-2008 test suit. These functions are summarized as follows.

Unimodal Functions (2):

  • F1: Shifted Sphere Function

  • F2: Shifted Schwefel’s

Multimodal Functions (5):

  • F3: Shifted Rosenbrock’s Function

  • F4: Shifted Rastrigin’s Function

  • F5: Shifted Griewank’s Function

  • F6: Shifted Ackley’s Function

  • F7: FastFractal “DoubleDip” Function

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Aote, S.S., Raghuwanshi, M.M., Malik, L.G. (2016). Particle Swarm Optimization Based on the Winner’s Strategy. In: Panigrahi, B., Suganthan, P., Das, S., Satapathy, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2015. Lecture Notes in Computer Science(), vol 9873. Springer, Cham. https://doi.org/10.1007/978-3-319-48959-9_18

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  • DOI: https://doi.org/10.1007/978-3-319-48959-9_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48958-2

  • Online ISBN: 978-3-319-48959-9

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