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Self Adaptive Acceleration Factor in Particle Swarm Optimization

  • Shimpi Singh Jadon
  • Harish Sharma
  • Jagdish Chand Bansal
  • Ritu Tiwari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 201)

Abstract

Particle swarm optimization (PSO), which is one of the leading swarm intelligence algorithms, dominates other optimization algorithms in some fields but, it also has the drawbacks like it easily falls into local optima and suffers from slow convergence in the later stages. This paper proposes improved version of PSO called Self Adaptive Acceleration Factors in PSO (SAAFPSO) to balance between exploration and exploitation. We converted the constant acceleration factors used in standard PSO into function of particle’s fitness. If a particle is more fit then it gives more importance to global best particle and less to itself to avoid local convergence. In later stages, particles will be more fitter so all will move towards global best particle, thus achieved the convergence speed. Experiment is performed and compared with standard PSO and Artificial bee colony (ABC) on \(14\) unbiased benchmark optimization functions and one real world engineering optimization problem (known as pressure vessel design) and results show that proposed algorithm SAAFPSO dominates others.

Keywords

Particle swarm optimization Artificial bee colony  Swarm intelligence Acceleration factor Optimization 

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References

  1. B. Akay and D. Karaboga. A modified artificial bee colony algorithm for real-parameter optimization. Information Sciences, 2010.Google Scholar
  2. P. Angeline. Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences. In Evolutionary Programming VII, pages 601–610. Springer, 1998.Google Scholar
  3. M.S. Arumugam and MVC Rao. On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems. Discrete Dynamics in Nature and Society, 2006, 2006.Google Scholar
  4. J.C. Bansal and H. Sharma. Cognitive learning in differential evolution and its application to model order reduction problem for single-input single-output systems. Memetic Computing, pages 1–21, 2012.Google Scholar
  5. G. Ciuprina, D. Ioan, and I. Munteanu. Use of intelligent-particle swarm optimization in electromagnetics. Magnetics, IEEE Transactions on, 38(2):1037–1040, 2002.Google Scholar
  6. M. Clerc. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, volume 3. IEEE, 1999.Google Scholar
  7. K. Diwold, A. Aderhold, A. Scheidler, and M. Middendorf. Performance evaluation of artificial bee colony optimization and new selection schemes. Memetic Computing, pages 1–14, 2011.Google Scholar
  8. R. Eberhart and J. Kennedy. A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS’95., Proceedings of the Sixth International Symposium on, pages 39–43. Ieee, 1995.Google Scholar
  9. R.C. Eberhart and Y. Shi. Comparing inertia weights and constriction factors in particle swarm optimization. In Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, volume 1, pages 84–88. IEEE, 2000.Google Scholar
  10. R.C. Eberhart and Y. Shi. Tracking and optimizing dynamic systems with particle swarms. In Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, volume 1, pages 94–100. IEEE, 2001.Google Scholar
  11. M. El-Abd. Performance assessment of foraging algorithms vs. evolutionary algorithms. Information Sciences, 2011.Google Scholar
  12. J. Ememipour, M.M.S. Nejad, M.M. Ebadzadeh, and J. Rezanejad. Introduce a new inertia weight for particle swarm optimization. In Computer Sciences and Convergence Information Technology, 2009. ICCIT’09. Fourth International Conference on, pages 1650–1653. IEEE, 2009.Google Scholar
  13. W. Gai-yun and H. Dong-xue. Particle swarm optimization based on self-adaptive acceleration factors. In Genetic and Evolutionary Computing, 2009. WGEC’09. 3rd International Conference on, pages 637–640. IEEE, 2009.Google Scholar
  14. D. Karaboga and B. Basturk. Artificial bee colony (abc) optimization algorithm for solving constrained optimization problems. Foundations of Fuzzy Logic and, Soft Computing, pp. 789–798, 2007.Google Scholar
  15. J. Kennedy and R. Eberhart. Particle swarm optimization. In Neural Networks, 1995. Proceedings., IEEE International Conference on, volume 4, pages 1942–1948. IEEE, 1995.Google Scholar
  16. J.J. Kim, S.Y. Park, and J.J. Lee. Experience repository based particle swarm optimization for evolutionary robotics. In ICCAS-SICE, 2009, pages 2540–2544. IEEE, 2009.Google Scholar
  17. H.R. Li and Y.L. Gao. Particle Swarm Optimization Algorithm with Exponent Decreasing Inertia Weight and Stochastic Mutation. In | 2009 Second International Conference on Information and Computing Science, pages 66–69. IEEE, 2009.Google Scholar
  18. X. D. Li and A. P. Engelbrecht. Particle swarm optimization: An introduction and its recent developments. Genetic Evol. Comput. Conf., pages 3391–3414, 2007.Google Scholar
  19. J.J. Liang, AK Qin, P.N. Suganthan, and S. Baskar. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. Evolutionary Computation, IEEE Transactions on, 10(3):281–295, 2006.Google Scholar
  20. R.F. Malik, T.A. Rahman, S.Z.M. Hashim, and R. Ngah. New particle swarm optimizer with sigmoid increasing inertia weight. International Journal of Computer Science and Security (IJCSS), 1(2):pages 35, 2007.Google Scholar
  21. S. Rahnamayan, H.R. Tizhoosh, and M.M.A. Salama. Opposition-based differential evolution. Evolutionary Computation, IEEE Transactions on, 12(1):64–79, 2008.Google Scholar
  22. A. Ratnaweera, S.K. Halgamuge, and H.C. Watson. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. Evolutionary Computation, IEEE Transactions on, 8(3):240–255, 2004.Google Scholar
  23. J. Kennedy R.C. Eberhart. A new optimizer using particle swarm theory, proceedings of 6th symp. micro machine and human science. In Proceedings of 6th symp. Micro Machine and Human Science, pages 39–43. Nagoya, Japan, 1995.Google Scholar
  24. H. Sharma, J. Bansal, and K. Arya. Dynamic scaling factor based differential evolution algorithm. In Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20–22, 2011, pages 73–85. Springer, 2012.Google Scholar
  25. Y. Shi and R. Eberhart. A modified particle swarm optimizer. In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, pages 69–73. IEEE, 1998.Google Scholar
  26. Y. Shi and R. Eberhart. Parameter selection in particle swarm optimization. In Evolutionary Programming VII, pages 591–600. Springer, 1998.Google Scholar
  27. P.N. Suganthan. Particle swarm optimiser with neighbourhood operator. In Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, volume 3. IEEE, 1999.Google Scholar
  28. X. Wang, XZ Gao, and SJ Ovaska. A simulated annealing-based immune optimization method. In Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning, Porvoo, Finland, pages 41–47, 2008.Google Scholar
  29. D.F. Williamson, R.A. Parker, and J.S. Kendrick. The box plot: a simple visual method to interpret data. Annals of internal medicine, 110(11):916, 1989.Google Scholar
  30. J. Xin, G. Chen, and Y. Hai. A Particle Swarm Optimizer with Multi-stage Linearly-Decreasing Inertia Weight. In Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on, volume 1, pages 505–508. IEEE, 2009.Google Scholar
  31. Z.H. Zhan, J. Zhang, Y. Li, and H.S.H. Chung. Adaptive particle swarm optimization. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 39(6):1362–1381, 2009.Google Scholar
  32. W. Zhang, H. Li, Z. Zhang, and H. Wang. The selection of acceleration factors for improving stability of particle swarm optimization. In Natural Computation, 2008. ICNC’08. Fourth International Conference on, volume 1, pages 376–380. IEEE, 2008.Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  • Shimpi Singh Jadon
    • 1
  • Harish Sharma
    • 1
  • Jagdish Chand Bansal
    • 1
  • Ritu Tiwari
    • 1
  1. 1.ABV-Indian Institute of Information Technology and ManagementGwaliorIndia

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