Adaptive Random Link PSO with Link Change Variations and Confinement Handling

Автор: Snehal Mohan Kamalapur, Varsha Hemant Patil

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 1 vol.7, 2014 года.

Бесплатный доступ

Particle Swarm Optimization is swarm based optimization technique. Swarm consists of particles and the particles fly through the problem space in Particle Swarm Optimization (PSO). Confinement methods and parameters such as Inertia Weight, Neighborhood of the particle have major impact on PSO performance. The paper presents variations of the PSO with adaptive random link neighborhood. The work carried out considers linearly decreasing inertia weight and different confinement methods. The performance of adaptive random link PSO by geometrical updation of velocity with confinement methods is tested here.

Adaptive Random Link, Confinement, Inertia Weight, Neighborhood, SPSO

Короткий адрес: https://sciup.org/15010647

IDR: 15010647

Список литературы Adaptive Random Link PSO with Link Change Variations and Confinement Handling

  • J. Kennedy, R.C. Eberhart, “Particle swarm optimization,” Proceedings of the IEEE International Conference on Neural Networks, Piscataway, NJ, USA, 1995, Vol. 4, pp. 1942 – 1948
  • R.C. Eberhart, J. Kennedy, "A new optimizer using particle swarm theory", Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995, pp. 39 – 43
  • J. Kennedy, R. C. Eberhart, and Y. H. Shi, Swarm Intelligence.San Mateo, CA: Morgan Kaufmann, 2001.
  • Maurice Clerc, “A method to improve Standard PSO,” Tech. Rep. MC2009, pp. 03-13, 2009, http://clerc.maurice.free.fr/pso/Design efficient PSO.pdf
  • Maurice Clerc, “Standard Particle Swarm Optimisation,” Particle Swarm Central, Tech. Rep., 2012, http://clerc.maurice.free.fr/pso/SPSO descriptions. pdf
  • Maurice Clerc, “Beyond standard particle swarm optimisation,” International Journal of Swarm Intelligence Research, vol. 1, no. 4, pp. 46–61, 2010.
  • Zambrano-Bigiarini, M. Clerc, Rojas, “Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements”. IEEE Congress on Evolutionary Computation (CEC) 2013 ,Digital Object Identifier: 10.1109/CEC.2013.6557848 , Page(s): 2337 – 2344
  • PSC, “Particle Swarm Central,” 2013, http://www.particleswarm.info/.
  • Momin Jamil and Xin-She Yang, A literature survey of benchmark functions for global optimization problems, Int. Journal of Mathematical Modelling and Numerical Optimisation}, Vol. 4, No. 2, pp. 150--194 (2013)
  • Christopher K. Monson and Kevin D. Seppi. Exposing Origin-Seeking Bias in PSO. In GECCO'05, pages 241_248, Washington, DC, USA, 2005.
  • William M. Spears, Derek T. Green, and Diana F. Spears. Biases in particle swarm optimization. International Journal of Swarm Intelligence Research, 1(2):34_57, 2010
  • Yuhui Shi , Eberhart, R, “A modified particle swarm optimizer “, Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence, 4-9 May 1998 PP 69 - 73
  • R. Poli, J. Kennedy, and T. Blackwell. Particle swarm optimization. Swarm Intelligence, 1(1), 2007, pp. 33-57
  • Y. Shi, and R.C. Eberhart, “Empirical study of particle swarm optimization”, Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1945 – 1950
  • Y. Zheng, et. al., “Empirical study of particle swarm optimizer with an increasing inertia weight”, Proceeding of the IEEE Congress on Evolutionary Computation, 2003
  • Huailiang Liu, Ruijuan Su, Ying Gao , Ruoning Xu, Improved Particle Swarm Optimization Using Two Novel Parallel Inertia Weights, Second International Conference on Intelligent Computation Technology and Automation, IEEE 2009pp 185-188
  • A. Adriansyah, and S.H.M. Amin, “Analytical and empirical study of particle swarm optimization with a sigmoid decreasing inertia weight”, Regional Conference on Engineering and Science, Johor, 2006
  • A.Nikabadi, M.Ebadzadeh , “Particle swarm optimization algorithms with adaptive Inertia Weight : A survey of the state of the art and a Novel method”, IEEE journal of evolutionary computation , 2008
  • Gao Yue-lin, Duan Yu-hong, “A New Particle Swarm Optimization Algorithm with Random Inertia Weight and Evolution Strategy” International Conference on Computational Intelligence and Security Workshops, IEEE 2007
  • Y. Feng, G.F. Teng, A.X. Wang, and Y.M. Yao., “Chaotic Inertia Weight in Particle Swarm Optimization”, In Innovative Computing, Information and Control, 2007. ICICIC’07. Second International Conference on, page 475. IEEE, 2008
  • K. Kentzoglanakis and M. Poole., “Particle swarm optimization with an oscillating Inertia Weight”, In Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pages 1749–1750. ACM, 2009
  • Y. Gao, X. An, and J. Liu., “A Particle Swarm Optimization Algorithm with Logarithm Decreasing Inertia Weight and Chaos Mutation”, In Computational Intelligence and Security, 2008. CIS’08. International Conference on, volume 1,IEEE, 2008, pp. 61-65
  • G. Chen, X. Huang, J. Jia, and Z. Min., Natural exponential Inertia Weight strategy in particle swarm optimization, In Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on, volume 1, pages 3672–3675. IEEE, 2006
  • Hui-rong Li, Yue-lin Gao, Particle Swarm Optimization Algorithm with Adaptive Threshold Mutation, in the proceeding of International Conference on Computational Intelligence and Security, Beijing, China, December 11-December 14 2009, ISBN: 978-0-7695-3931-7,pp. 129-132
  • 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,IEEE, 2009, pp 66-69
  • Shi Y. Eberhart RC, Fuzzy adaptive particle swarm optimization, Evolutionary Computation, 2001. Proceedings of the 2001 Congress, Volume 1, Issue, 2001, pp.101 – 106
  • J. Kennedy and R. Mendes, Population structure and particle swarm performance, in Proc. 2002 Cong. Evol. Comput., 2002, pp.1671-1675
  • Rui Mendes, James Kennedy, and José Neves, The Fully Informed Particle Swarm: Simpler, Maybe Better, IEEE Transactions On Evolutionary Computation, Vol. 8, No. 3, June 2004
  • Helwig S, Branke, J. ,Mostaghim, S.M., "Experimental Analysis of Bound Handling Techniques in Particle Swarm Optimization", Evolutionary Computation, IEEE Transactions on (Volume:17 , Issue: 2 ), April 2013, pp. 259-271
  • Maurice clerc, Particle Swarm Optimization, ISTE ( International Scientific and Technical Encyclopedia, 2006
  • J. Kennedy and R. Mendes, Population structure and particle swarm performance, in Proc. 2002 Cong. Evolutionary Computation, 2002, pp.1671-1675.
Еще
Статья научная