The non-clique particle swarm optimizer

ABSTRACT

Neighborhood topology of particle swarm affects the performance of PSO. Through analyzing the graph properties of typical neighborhood topologies, this paper presents a non-clique static neighborhood topology which has lower clustering coefficient and smaller average path length. Compared to other topologies with the same neighborhood size and population size, the proposed topology has more uniform neighbor distribution. The experiment results demonstrate that the PSO based on the non-clique topology has great superiority both in robustness and efficiency.

REFERENCES

• 1: Kennedy, J. and Eberhart, R. C. 1995. Particle swarm optimization. In Proc.IEEE Int. Conf. Neural Networks. IEEE Press, Perth, 1942--1948.
• 2: del Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.-C., and Harley, R.G. 2008. Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems. IEEE Transaction on Evolutionary Computation 12 (2008), 171--195.
• 3: DeBao Chen , ChunXia Zhao, Particle swarm optimization with adaptive population size and its application, Applied Soft Computing, v.9 n.1, p.39-48, January, 2009 [doi>10.1016/j.asoc.2008.03.001]
• 4: J. Kennedy , R. Mendes, Population structure and particle swarm performance, Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress, p.1671-1676, May 12-17, 2002
• 5: Kennedy, J. and Mendes, R. 2006. Neighborhood topologies in fully informed and best-of-neighborhood particle swarms. IEEE Transaction on Systems, Man, and Cybernetics -- Part C: Applications and Reviews 36 (2006), 515--519.
• 6: Suganthan, P.N. 1999. Particle swarm optimizer with neighborhood operator. In Proc. Congr. Evolutionary Computation (CEC1999), 1958--1962.
• 7: Kennedy, J. 2000. Stereotyping: improving particle swarm performance with cluster analysis. In Proc. Congr. Evolutionary Computation. IEEE Press, La Jolla, 1507--1512.
• 8: Janson, S. and Middendorf, M. 2005. A hierarchical particle swarm optimizer and its adaptive variant. IEEE Transaction on Systems, Man, and Cybernetics -- Part B 35 (2005), 1272--1282.
• 9: Johannes Jordan , Sabine Helwig , Rolf Wanka, Social interaction in particle swarm optimization, the ranked FIPS, and adaptive multi-swarms, Proceedings of the 10th annual conference on Genetic and evolutionary computation, July 12-16, 2008, Atlanta, GA, USA [doi>10.1145/1389095.1389103]
• 10: Wang, X.F. and Chen, G. 2003. Complex networks: small-world, scale-free and beyond. IEEE Circuits and Systems Magazine 3, 1 (2003), 6--20.
• 11: Ioan Cristian Trelea, The particle swarm optimization algorithm: convergence analysis and parameter selection, Information Processing Letters, v.85 n.6, p.317-325, 31 March 2003 [doi>10.1016/S0020-0190(02)00447-7]
• 12: Mendes, R. 2004. Population topologies and their influence in particle swarm performance. PhD thesis, Universidade do Minho, Portugal.

INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization
          Subjects: Unconstrained optimization

Additional Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization
          Subjects: Nonlinear programming

General Terms:
Algorithms

Keywords:
graph theory, neighborhood topology, particle swarm optimization