Repulsive Self-Adaptive Acceleration Particle Swarm Optimization Approach

Simone A. Ludwig 1
  • 1 Department of Computer Science, North Dakota State University, Fargo, ND, USA


Adaptive Particle Swarm Optimization (PSO) variants have become popular in recent years. The main idea of these adaptive PSO variants is that they adaptively change their search behavior during the optimization process based on information gathered during the run. Adaptive PSO variants have shown to be able to solve a wide range of difficult optimization problems efficiently and effectively. In this paper we propose a Repulsive Self-adaptive Acceleration PSO (RSAPSO) variant that adaptively optimizes the velocity weights of every particle at every iteration. The velocity weights include the acceleration constants as well as the inertia weight that are responsible for the balance between exploration and exploitation. Our proposed RSAPSO variant optimizes the velocity weights that are then used to search for the optimal solution of the problem (e.g., benchmark function). We compare RSAPSO to four known adaptive PSO variants (decreasing weight PSO, time-varying acceleration coefficients PSO, guaranteed convergence PSO, and attractive and repulsive PSO) on twenty benchmark problems. The results show that RSAPSO achives better results compared to the known PSO variants on difficult optimization problems that require large numbers of function evaluations.

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  • [1] J. Kennedy and R. Eberhart, Particle swarm optimization, Proceedings of IEEE International Conference on Neural Networks, 4:1942–1948, 1995.

  • [2] A. Engelbrecht, Computational Intelligence: An Introduction, 2nd Edition, Wiley, 2007.

  • [3] A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, IEEE Transaction on Evolutionary Computation, 8(3):240–255, 2004.

  • [4] X. Li, H. Fu, and C. Zhang, A self-adaptive particle swarm optimization algorithm, Proceedings of the 2008 International Conference on Computer Science and Software Engineering, 186–189, 2008.

  • [5] I. C. Trelea, The Particle Swarm Optimization Algorithm: Convergence Analysis and Parameter Selection, Information Processing Letters, 85(6), 317-325, 2003.

  • [6] F. Van den Bergh and A. P. Engelbrecht, A Study of Particle Swarm Optimization Particle Trajectories, Information Sciences, 176(8), 937-971, 2006.

  • [7] C. K. Monson, K. D. Seppi, Exposing Origin-Seeking Bias in PSO, Proceedings of GECCO’05, pp. 241-248, 2005.

  • [8] F. Van den Bergh and A. P. Engelbrecht, A new locally convergent particle swarm optimiser, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 3:94–99, 2002.

  • [9] F. Schutte, A. A. Groenwold, A Study of Global Optimization using Particle Swarms, Journal of Global Optimization, 31, 93-108, 2005.

  • [10] A. Chatterjee, P. Siarry, Nonlinear inertial weight variation for dynamic adaptation in particle swarm optimization, Journal of Computer Operation Research, 33(3), 859-871, 2004.

  • [11] X. Yang, J. Yuan, J. Yuan, and H. Mao, A modified particle swarm optimizer with dynamic adaptation, Applied Mathematics and Computation, 189(2):1205–1213, 2007.

  • [12] J. Zhu, J. Zhao, and X. Li, A new adaptive particle swarm optimization algorithm, International Workshop on Modelling, Simulation and Optimization, 456–458, 2008.

  • [13] Y. Bo, Z. Ding-Xue, and L. Rui-Quan, A modified particle swarm optimization algorithm with dynamic adaptive, 2007 Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2:346–349, 2007.

  • [14] T. Yamaguchi and K. Yasuda, Adaptive particle swarm optimization; self-coordinating mechanism with updating information, IEEE International Conference on Systems, Man and Cybernetics, 2006. SMC ’06, 3:2303–2308, 2006.

  • [15] T. Yamaguchi, N. Iwasaki, and K. Yasuda, Adaptive particle swarm optimization using information about global best, IEEE Transactions on Electronics, Information and Systems, 126:270–276, 2006.

  • [16] K. Yasruda, K. Yazawa, and M. Motoki, Particle swarm optimization with parameter self-adjusting mechanism, IEEE Transactions on Electrical and Electronic Engineering, 5(2):256–257, 2010.

  • [17] S. A. Ludwig, Towards A Repulsive and Adaptive Particle Swarm Optimization Algorithm, Proceedings of Genetic and Evolutionary Computation Conference (ACM GECCO), Amsterdam, Netherlands, July 2013 (short paper).

  • [18] J. Riget and J. S. Vesterstrø, A diversity-guided particle swarm optimizer—The ARPSO, EVALife Technical Report no. 2002-2, 2002.

  • [19] A. Ide and K. Yasuda, A basic study of adaptive particle swarm optimization, Denki Gakkai Ronbunshi, Electrical Engineering in Japan, 151(3):41–49, 2005.

  • [20] M. Meissner, M. Schmuker, and G. Schneider, Optimized particle swarm optimization (OPSO) and its application to artificial neural network training, BMC Bioinformatics, 7(1):125, 2006.

  • [21] X. Cai, Z. Cui, J. Zeng, and Y. Tan, Self-learning particle swarm optimization based on environmental feedback, Innovative Computing, Information and Control, 2007. ICICIC ’07, page 570, 2007.

  • [22] Z. H. Zhan and J. Zhang, Proceedings of the 6th International Conference on Ant Colony Optimization and Swarm Intelligence, pages 227–234, 2008.

  • [23] M. Clerc, Semi-continuous challenge,, April 2004.

  • [24] M. Molga and C. Smutnicki, Test functions for optimization needs,, 2005.

  • [25] Z. H. Zhan, J. Zhang, Y. Li, and H. S. H. Chung, Adaptive particle swarm optimization, IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 39(6):1362–1381, 2009.

  • [26] M. Friedman, The use of ranks to avoid the assumption of normality implicit in the analysis of variance, Journal of the American Statistical Association, vol. 32, no. 200, pp. 675–701, 1937.


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