Solving large-scale problems using multi-swarm particle swarm approach

  • Authors

    • Sinan Q. Salih Faculty of Computer Systems & Software Engineering, Univerisiti Malaysia Pahang, Gambang, Pahang , Malaysia
    • Abdul Rahman A. Alsewari Faculty of Computer Systems & Software Engineering, Univerisiti Malaysia Pahang, Gambang, Pahang , Malaysia
    2018-08-21
    https://doi.org/10.14419/ijet.v7i3.14742
  • Particle Swarm Optimization, Multi-Swarm Optimization, Meeting Room Approach, Large-Scale.

  • Abstract

    Several metaheuristics have been previously proposed and several improvements have been implemented as well. Most of these methods were either inspired by nature or by the behavior of certain swarms such as birds, ants, bees, or even bats. In the metaheuristics, two key components (exploration and exploitation) are significant and their interaction can significantly affect the efficiency of a metaheuristic. How-ever, there is no rule on how to balance these important components. In this paper, a new balancing mechanism based on multi-swarm approach is proposed for balancing exploration and exploitation in metaheuristics. The new approach is inspired by the concept of a group(s) of people controlled by their leader(s). The leaders of the groups communicate in a meeting room where the overall best leader makes the final decisions. The proposed approach applied on Particle Swarm Optimization (PSO) to balance the exploration and exploitation search called multi-swarm cooperative PSO (MPSO). The proposed approach strived to scale up the application of the (PSO) algorithm towards solving large-scale optimization tasks of up to 1000 real-valued variables. In the simulation part, several benchmark functions were per-formed with different numbers of dimensions. The proposed algorithm was tested on several test functions, with four different number of dimensions (100, 500, and 1000) it was evaluated in terms of performance efficiency and compared to standard PSO (SPSO), and master-salve PSO algorithm. The results showed that the proposed PSO algorithm outperformed the other algorithms in terms of the optimal solutions and the convergence.

     

     

  • References

    1. [1] I. Boussaïd, J. Lepagnot, P. Siarry, A survey on optimization metaheuristics, Inf. Sci. (Ny).237 (2013) 82–117. https://doi.org/10.1016/j.ins.2013.02.041.

      [2] I. Fister, X.S. Yang, J. Brest, D. Fister, A brief review of nature-inspired algorithms for optimization, Elektroteh. Vestnik/Electrotechnical Rev. 80 (2013) 116–122.

      [3] A. Slowik, H. Kwasnicka, Nature Inspired Methods and Their Industry Applications – Swarm Intelligence Algorithms, IEEE Trans. Ind. Informatics.14 (2018) 1004–1015. https://doi.org/10.1109/TII.2017.2786782.

      [4] X.S. Yang, Nature-Inspired Optimization Algorithms, 2014. https://doi.org/10.1016/C2013-0-01368-0.

      [5] X.S. Yang, Nature-Inspired Metaheuristic Algorithms, 2010. https://doi.org/10.1016/B978-0-12-416743-8.00005-1.

      [6] X.S. Yang, S. Deb, Y.-X. Zhao, S. Fong, X. He, Swarm intelligence: past, present and future, Soft Comput. (2017). https://doi.org/10.1007/s00500-017-2810-5.

      [7] B. Xue, M. Zhang, W.N. Browne, Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms, Appl. Soft Comput. J. 18 (2014) 261–276. https://doi.org/10.1016/j.asoc.2013.09.018.

      [8] R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, MHS’95. Proc. Sixth Int. Symp. Micro Mach. Hum. Sci. (1995) 39–43. https://doi.org/10.1109/MHS.1995.494215.

      [9] J. Kennedy, R. Eberhart, Particle swarm optimization, Neural Networks, 1995. Proceedings. IEEE Int. Conf. 4 (1995) 1942–1948 vol.4. https://doi.org/10.1109/ICNN.1995.488968.

      [10] İ.B. Aydilek, A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems, Appl. Soft Comput. J. 66 (2018) 232–249. https://doi.org/10.1016/j.asoc.2018.02.025.

      [11] K. Premalatha, a M. Natarajan, Hybrid PSO and GA for Global Maximization, Int. J. Open Probl. Compt. Math.2 (2009) 597–608. Doi: 1998-6262.

      [12] D. Chen, J. Chen, H. Jiang, F. Zou, T. Liu, An improved PSO algorithm based on particle exploration for function optimization and the modeling of chaotic systems, Soft Comput.19 (2015) 3071–3081. https://doi.org/10.1007/s00500-014-1469-4.

      [13] X.S. Yang, Metaheuristic optimization: algorithm analysis and open problems, in: Int. Symp. Exp. Algorithms, 2011: pp. 21–32. https://doi.org/10.1007/978-3-642-20662-7_2.

      [14] Y. Shi, R. Eberhart, a Modified Particle Swarm Optimizer, Evol. Comput. Proceedings, 1998. IEEE World Congr. Comput. Intell. 1998 IEEE Int. Conf. (1998) 69–73. https://doi.org/10.1109/ICEC.1998.699146.

      [15] Y. Eberhart, R. C. and Shi, Tracking and optimizing dynamic systems with particle swarms, in: Proc. IEEE Congr. Evol. Comput. IEEE, Seoul, Korea, 2001: pp. 94–97.

      [16] R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, in: Micro Mach. Hum. Sci. 1995. MHS’95. Proc. Sixth Int. Symp., 1995: pp. 39–43. https://doi.org/10.1109/MHS.1995.494215.

      [17] M. Jamil, X.S. Yang, H.J.D. Zepernick, Test Functions for Global Optimization: A Comprehensive Survey, in: Swarm Intell. Bio-Inspired Comput. 2013: pp. 193–222. https://doi.org/10.1016/B978-0-12-405163-8.00008-9.

      [18] Y. Shi, R.C. Eberhart, Empirical study of particle swarm optimization, Evol. Comput. 1999. CEC 99. Proc. 1999 Congr.3 (1999) 1945–1950 Vol.3. https://doi.org/10.1109/CEC.1999.785511.

      [19] B. Niu, Y. Zhu, X. He, H. Wu, MCPSO: A multi-swarm cooperative particle swarm optimizer, Appl. Math. Comput.185 (2007) 1050–1062. https://doi.org/10.1016/j.amc.2006.07.026.

  • Downloads

  • How to Cite

    Q. Salih, S., & Rahman A. Alsewari, A. (2018). Solving large-scale problems using multi-swarm particle swarm approach. International Journal of Engineering & Technology, 7(3), 1725-1729. https://doi.org/10.14419/ijet.v7i3.14742

    Received date: 2018-06-28

    Accepted date: 2018-08-05

    Published date: 2018-08-21