Abstract
In this paper, several diversity measurements will be discussed and defined. As in other evolutionary algorithms, first the population position diversity will be discussed followed by the discussion and definition of population velocity diversity which is different from that in other evolutionary algorithms since only PSO has the velocity parameter. Furthermore, a diversity measurement called cognitive diversity is discussed and defined, which can reveal clustering information about where the current population of particles intends to move towards. The diversity of the current population of particles and the cognitive diversity together tell what the convergence/divergence stage the current population of particles is at and which stage it moves towards.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Eberhart R, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Piscataway: IEEE Service Center, 1995, 39–43
Kennedy J, Eberhart R. Particle swarm optimization. In: Procedings of IEEE International Conference on Neural Networks (ICNN), 1995, IV: 1942–1948
Eberhart R, Shi Y H. Comparison between genetic algorithms and particle swarm optimization. In: Porto V W, Saravanan N, Waagen D, Eiben A E, eds. Evolutionary Programming VII: Proceedings of 7th Annual Conference on Evolutionary Programming. Berlin: Springer-Verlag, 1998, 611–616
Eberhart R, Shi Y H. Computational Intelligence: Concepts to Implementations. Morgan Kaufmann Publishers, 2007
Kennedy J, Eberhart R, Shi Y H. Swarm Intelligence. Morgan Kaufmann Publishers, 2001
Shi Y H, Eberhart R. Parameter selection in particle swarm optimization. In: Proceedings of the 1998 Annual Conference on Evolutionary Computation, 1998, 591–600
Shi Y H, Eberhart R. A modified particle swarm optimizer. In: Proceedings of the 1998 IEEE International Conference on Evolutionary Computation. Piscataway: IEEE Press, 1998, 69–73
Shi Y H, Eberhart R, Chen Y B. Implementation of evolutionary fuzzy system. IEEE Transactions on Fuzzy Systems, 1999, 7(2): 109–119
Shi Y H, Eberhart R. Fuzzy adaptive particle swarm optimization, In: Proceedings of the 2001 Congress on Evolutionary Computation. Piscataway: IEEE Service Center, 2001, 101–106
Shi Y H, Eberhart R. Population diversity of particle swarm optimization. In: Proceedings of the 2008 Congress on Evolutionary Computation, 2008, 1063–1067
Fan H Y, Shi Y H. Study on Vmax of particle swarm optimization. In: Proceedings of the Workshop on Particle Swarm Optimization. Indianapolis: Purdue School of Engineering and Technology, IUPUI. April, 2001
Ratnaweera A, Halgamuge S, Watson H. Self-organizing hierarchical particle swarm optimizer with time varying accelerating Coefficients. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 240–255
Kennedy J, Mendes R. Population structure and particle swarm performance. In: Proceedings of the 2002 Congress on Evolutionary Computation. Honolulu, 2002
Mendes R, Kennedy J, Neves J. The fully informed particle swarm: simpler, maybe better. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 204–210
Parsopoulos K E, Vrahatis M N. Particle swarm optimization method for constrained optimization problems. In: Sincak P, et al, eds. Intelligent Technologies — Theory and Application, 2002, 214–220
Reyes-Sierra M, Coello Coello C A. Multi-objective particle swarm optimizers: a survey of the state-of-the-art. International Journal of Computational Intelligence Research, 2006, 2(3): 287–308
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Shi, Y., Eberhart, R. Monitoring of particle swarm optimization. Front. Comput. Sci. China 3, 31–37 (2009). https://doi.org/10.1007/s11704-009-0008-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11704-009-0008-4