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Particle swarm with self-organized criticality

Authors:

Carlos Miguel Fernandes,

Juan Merelo,

Agostinho Rosa,

Francisco FernándezAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

Pages 1397 - 1398

https://doi.org/10.1145/2330784.2330950

Published: 07 July 2012 Publication History

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Abstract

This paper introduces a strategy for controlling the parameters of the Particle Swarm Optimization (PSO) based on a Self-Organized Criticality (SOC) system known as the Bak-Sneppen model of co-evolution. An experimental setup compares the new algorithm with a state-of-the-art PSO with dynamic variation of the inertia weight value and perturbation of the particles' positions.

References

[1]

Bak, P., and Sneppen, K. 1993. Punctuated Equilibrium and Criticality in a Simple Model of Evolution. Physical Review Letters, Vol. 71(24), 4083--4086.

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[2]

Fernandes, C.M., Merelo, J.J., Ramos, V., Rosa, A.C. 2008. A Self-Organized Criticality Mutation Operator for Dynamic Optimization Problems. In Proceedings of the 2008 Genetic and Evolutionary Computation Conference, ACM, 937--944.

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[3]

Kennedy, J.; Eberhart, R. 1995. Particle Swarm Optimization. In Proceedings of IEEE International Conference on Neural Networks, Vol.4, 1942--1948.

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Google Scholar

[4]

Løvbjerg, M., Krink, T. 2002. Extending particle swarm optimizers with self-organized criticality. In Proc. of the 2002 IEEE Congress on Evolutionary Computation, Vol. 2, IEEE Computer Society, 1588--1593.

Digital Library

Google Scholar

[5]

Shi, Y. Eberhart, R.C. 1998. A Modified Particle Swarm Optimizer. In Pro. of IEEE 1998 International Conference on Evolutionary Computation, IEEE Press, 69--73.

Crossref

Google Scholar

[6]

Suresh, K., Ghosh, S., Kundu, D., Sen, A., Das, S., Abraham, A. 2008. Inertia-Adaptive Particle Swarm Optimizer for Improved Global Search. In Proceedings of the 8th Inter. Conference on Intelligent Systems Design and Applications, Vol. 2. IEEE, Washington, DC, USA, 253--258.

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Published In

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

July 2012

1586 pages

ISBN:9781450311786

DOI:10.1145/2330784

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2012

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GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Self-Adaptive Particle Swarm Optimization

Self regulating particle swarm optimization algorithm

Euclidean Particle Swarm Optimization

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