Abstract
Topology plays an important role for Particle Swarm Optimization (PSO) to achieve good optimization performance. It is difficult to find one topology structure for the particles to achieve better optimization performance than the others since the optimization performance not only depends on the searching abilities of the particles, also depends on the type of the optimization problems. Three elitist set based PSO algorithm without using explicit topology structure is proposed in this paper. An elitist set, which is based on the individual best experience, is used to communicate among the particles. Moreover, to avoid the premature of the particles, different statistical methods have been used in these three proposed methods. The performance of the proposed PSOs is compared with the results of the standard PSO 2011 and several PSO with different topologies, and the simulation results and comparisons demonstrate that the proposed PSO with adaptive probabilistic preference can achieve good optimization performance.
Article PDF
Avoid common mistakes on your manuscript.
References
C.M. Huang, C.J. Huang and M.L. Wang, A particle swarm optimisation to identifying the ARMAX model for short-term load forecasting, IEEE Transaction on Power Systems, 20 (2) (2005) 1126–1133.
M. Clerc, Particle Swarm Optimisation. (ISTE Publishing Company, 2006).
N. Nedjah, and L.D.M. Mourelle, Systems Engineering Using Particle Swarm Optimisation, (Nova Science Publishers, 2007).
Y. del Valle and G.K. Venayagamoorthy, Particle Swarm Optimisation: Basic Concepts, Variants and Applications in Power Systems, IEEE Transactions on Evolutionary Computation, 12 (2) (2008) 171–195.
J. Kennedy and E.C. Eberhart and Y.H. Shi, Swarm intelligence. (San Francisco: Morgan Kaufmann Publisher, 2001).
J. Kennedy, Small worlds and mega-minds: Effects of neighbourhood topology on particle swarm performance. in Proc. of IEEE Congress Evolutionary Computation, July, 3 (Bureau of Labor Statistics, USA, 1999) 1931– 1938.
J. Kennedy and R. Mendes, Population structure and particle swarm performance, in Proc. of IEEE Congress Evolutionary Computation, (Honolulu, HI, USA, May 2002), 2, pp. 1671–1676.
J. Kennedy, M. Clerc, et. al., Particle Swarm Central, http://www.particleswarm.info/Programs.html, 2012.
M. Clerc and J. Kennedy, The particle Swarm: explosion, stability, and convergence in multi-dimension complex space, IEEE Transactions on Evolutionary Computation, 6 (1) (2002) 58–73.
B. Liu, L. Wang, Y.H. Jin, F. Tang, and D.X. Huang, Improved particle swarm optimization combined with chaos, Chaos, Solitons and Fractals, 25 (2005) 1261–1271.
J. Kennedy and R.C. Eberhart, Particle swarm optimisation, in Proc. of. IEEE International Conference Neural Networks, (Perth, Australia, 1995 ) pp. 1942– 1948.
M. Dorigo, Optimization, Learning and Natural Algorithms, PhD thesis, Politecnico di Milano, Italie, 1992.
S. Rahnamayan, H.R. Tizhoosh, M.M.A. Salama, Opposition-based differential evoution, IEEE transactions On Evolutionary Computation, 12(1) (2008) 64–79.
Y. Zhang, S. Wang, and G. Ji, A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications, Mathematical Problems in Engineering, (2015), Article ID 931256, 38 pages.
I.F. Jr., M. Perc, K. Ljubič, S.M. Kamal, and A. Iglesias, Particle swarm optimization for automatic creation of complex graphic characters, Chaos, Solitons & Fractals, 73 (2015) 29–35.
Z. Yaqin, L. Beizhi, and W. Lv, Study on job-shop scheduling with multi-objectives based on genetic algorithms, in Proc. of the International Conference on Computer Application and System Modelling (ICCASM ’10), (Taiyuan, China, October 2010), pp. v10294– v10298.
N.H. Moin, O.C. Sin, M. Omar, Hybrid Genetic Algorithm with Multiparents Crossover for Job Shop Scheduling Problems, Mathematical Problems in Engineering, Volume 2015 (2015), Article ID 210680, 12 pages
J. Gao, R. Chen and W. Deng, An efficient tabu search algorithm for the distributed permutation flowshop scheduling problem, International Journal of Production Research, 51 ( 3) ( 2013) 641–651.
Q.K. Pan and R. Ruiz, An effective iterated greedy algorithm for the mixed no-idle permutation flowshop scheduling problem, Omega, 44 (2014) 41–50.
T. Celso, Miasaki, M.C. F. Edgar and A.R. Ruben, Transmission Network Expansion Planning Considering Phase-Shifter Transformers, Journal of Electrical and Computer Engineering, 2012 (2012), Article ID 527258, 10 pages
S. Das and P.N. Suganthan, Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems, Technical Report, Jadavpur University, India and Nanyang Technological University 2010.
Q. Ni, X. Yin, K. Tian and Y. Zhai, Particle swarm optimization with dynamic random population topology strategies for a generalized portfolio selection problem Natural Computing, Natural Computing: an international journal, 16 (1) (2017) , 31–44.
D. Chen, R. Zhang, C. Yao, Z. Zhao, Dynamic topology multi force particle swarm optimization algorithm and its application, Chinese Journal of Mechanical Engineering, 29 (1) (2016) 124–135.
C.M. Fernandes, J.L.J. Laredo, J.J. Merelo, C. Cotta, A.C. Rosa, Particle swarm optimization with dynamic topology and conservation of evaluations, Studies in Computational Intelligence, 620 (2016) 97–111.
D.J. Watts, and S.H. Strogatz, Collective dynamics of ‘small-world’ networks. Nature, 393 (1998) 440–442.
C. Liu, Du WB, Wang WX, Particle swarm optimization with scale-free interactions, Plos One, 9(5) (2014) 1–8.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
About this article
Cite this article
Sun, Y., Wang, Z. Elitism set based particle swarm optimization and its application. Int J Comput Intell Syst 10, 1316–1329 (2017). https://doi.org/10.2991/ijcis.10.1.92
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.2991/ijcis.10.1.92