- Cédric Leboucher16,
- Patrick Siarry17,
- Stéphane Le Ménec16,
- Hyo-Sang Shin18,
- Rachid Chelouah19 &
- …
- Antonios Tsourdos18
Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 8472))
Included in the following conference series:
825Accesses
Abstract
This paper proposes to reduce the computational time of an algorithm based on the combination of the Evolutionary Game Theory (EGT) and the Particle Swarm Optimisation (PSO), named C-EGPSO, by using Neural Networks (NN) in order to lighten the computation of the identified heavy part of the C-EGPSO. This computationally burdensome task is the resolution of the EGT part that consists in solving iteratively a differential equation in order to optimally adapt the direction search and the size step of the PSO at each iteration. Therefore, it is proposed to use NN to learn the solution of this differential equation according to the initial conditions in order to gain a precious time.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 5491
- Price includes VAT (Japan)
- Softcover Book
- JPY 6864
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, USA, pp. 1942–1948 (1995)
Kennedy, J., Eberhart, R.: A discrete binary version of the particle swarm algorithm. In: The 1997 IEEE International Conference on Systems, Man, and Cybernetics, vol. 5, Orlando, USA, pp. 4104–4108, October 1997
Hu, W., Song, J., Li, W.: A new PSO scheduling simulation algorithm based on an intelligent compensation particle position rounding off. In: ICNC ’08 Proceedings of the 2008 Fourth International Conference on Natural Computation, vol. 1, Jinan, China, pp. 145–149, October 2008
Junker, U.: Air traffic flow management with heuristic repair. The Knowledge Engineering Review, vol. 0, pp. 1–24 (2004)
Shaa, D., H.H., L.: A multi-objective PSO for job-shop scheduling problems. Expert Systems with Applications, vol. 37, pp. 1065–1070 (2010)
Badamchizadeh, M., Madani, K.: Applying modified discrete particle swarm optimization algorithm and genetic algorithm for system identification. In: Computer and Automation Engineering (ICCAE), 2010, vol. 5, Singapore, Republic of Singapore, pp. 354–358, February 2010
Eberhart, R., Shi, Y.: Comparison between genetic algorithms and particle swarm optimization. In: Proceedings of the 7th International Conference on Evolutionary Programming, San Diego, USA, pp. 611–616, March 1998
Liaoa, C., Tsengb, C., Luarnb, P.: A discrete version of particle swarm optimization for flowshop scheduling problems. Computers & Operations Research34, 3099–3111 (2007)
Hassan, R., Cohanim, B., de Weck, O., Venter, G.: A comparison of particle swarm optimization and the genetic algorithm. Tech. rep., Massachusetts Institute of Technology, Cambridge, MA, 02139 (2004)
Clerc, M.: Standard particle swarm optimisation, Tech. rep., maurice.clerc@Writeme.com (2012)
Leboucher, C., Shin, H.-S., Siarry, P., Chelouah, R., Ménec, S.L., Tsourdos, A.: A Two-Step Optimisation Method for Dynamic Weapon Target Assignment Problem. Recent Advances on Meta-Heuristics and Their Application to Real Scenarios, InTech (2013)
Congress on Evolutionary Computation, Edinburgh, UK, September 2005
Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Tech. rep., Technical Report, Nanyang Technological University, Singapore and KanGAL Report Number 2005005 (Kanpur Genetic Algorithms Laboratory, IIT Kanpur) (2005)
Leboucher, C., Chelouah, R., Siarry, P., Ménec, S.L.: A swarm intelligence method combined to evolutionary game theory applied to resource allocation problem. In: International conference on swarm intelligence, Cergy, France, June 2011
Leboucher, C., Chelouah, R., Siarry, P., Le Ménec, S.: A swarm intelligence method combined to evolutionary game theory applied to the resources allocation problem. International Journal of Swarm Intelligence Research (IJSIR)3(2), 20–38 (2012)
Mendes, R.: Population topologies and their influence in particle swarm performance. PhD thesis, University Minho (2004)
Clerc, M.: Back to random topology. Tech. rep., maurice.clerc@Writeme.com (2007)
Taylor, P., Jonker, L.: Evolutionary stable strategies and game dynamics. Mathematical Bioscience40, 145–156 (1978)
Cressman, R.: Evolutionary Dynamics and Extensive Form Games. MIT Press (2003)
Sandholm, W.: Population Games and Evolutionary Dynamics. MIT Press (2010)
Floudas, C.A., Pardalos, P.M.: Encyclopedia of Optimization. Springer (2009)
McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics5(4), 115–133 (1943)
Ackley, D.H., Hinton, G.E., Sejnowski, T.J.: A learning algorithm for Boltzman machines. Cognitive Sciences9, 147–169 (1985)
Fausett, L.: Fundamentals of neural networks: Architectures, algorithms, and applications. Prentice Hall (1994)
Haykin, S.: Neural Networks: A comprehensive foundation. MacMillan College (1994)
Cichocki, A., Unbehauen, R.: Neural networks for optimization and signal processing. Wiley (1993)
Looi, C.-K.: Neural network methods in combinatorial optimization. Computational Operations Research19(3–4), 191–208 (1992)
Ansari, N., Hou, E.S.H., Yu, Y.: A new method to optimize the satellite broadcasting schedules using the mean field annealing of a Hopfield neural network. IEEE Transactions on Neural Networks6(2), 470–482 (1995)
Lagerholm, M., Peterson, C., Soderberg, B.: Airline crew scheduling with Potts neurons. Neural Computation9, 1589–1599 (1997)
Fang, L., Li, T.: Design of competition-based neural networks for combinatorial optimization. International Journal of Neural Systems1(3), 221–235 (1990)
Hopfield, J., Tank, D.W.: Neural computation of decisions in optimization problems. Biological Cybernetics52(3), 141–152 (1985)
Levy, B.C., Adam, M.B.: Global optimization with stochastic neural networks. In: First International Conference on Neural networks for Optimization and signal processing, San Diego, USA, pp. 681–690 (1987)
Abe, S., Kawakami, J., Hirasawa, K.: Solving inequality constrained combinatorial optimization problems by the Hopfield neural networks. Neural Networks5, 663–670 (1992)
MATLAB, Neural Networks Toolbox
Auger, A., Hansen, N.: A restart CMA evolution strategy with increasing population size. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1769–1776 (2005)
Sinha, A., Tiwari, S., Deb, K.: A population-based, steady-state procedure for real-parameter optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 1, pp. 514–521 (2005)
Author information
Authors and Affiliations
MBDA France, 1, avenue Réaumur, 92350, Le Plessis Robinson, France
Cédric Leboucher & Stéphane Le Ménec
Univ. de Paris-Est Créteil, Laboratoire Images, Signaux et Systèmes Intelligents, LiSSi (E.A. 3956), 122 rue Paul Armangot, 94400, Vitry sur Seine, France
Patrick Siarry
Cranfield University, College Road, Cranfield, Bedfordshire, MK43 0AL, United Kingdom
Hyo-Sang Shin & Antonios Tsourdos
EISTI, Avenue du Parc, 95000, Cergy, France
Rachid Chelouah
- Cédric Leboucher
You can also search for this author inPubMed Google Scholar
- Patrick Siarry
You can also search for this author inPubMed Google Scholar
- Stéphane Le Ménec
You can also search for this author inPubMed Google Scholar
- Hyo-Sang Shin
You can also search for this author inPubMed Google Scholar
- Rachid Chelouah
You can also search for this author inPubMed Google Scholar
- Antonios Tsourdos
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toCédric Leboucher.
Editor information
Editors and Affiliations
Univ Paris-Est Créteil UPEC LISSI, Vitry Sur Seine, France
Patrick Siarry
Université de Haute Alsace LMIA-INRIA Grand Est, Mulhouse, France
Lhassane Idoumghar
Université de Haute-Alsace, LMIA, Mulhouse, France
Julien Lepagnot
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Leboucher, C., Siarry, P., Le Ménec, S., Shin, HS., Chelouah, R., Tsourdos, A. (2014). An Enhanced Particle Swarm Optimisation Algorithm Combined with Neural Networks to Decrease Computational Time. In: Siarry, P., Idoumghar, L., Lepagnot, J. (eds) Swarm Intelligence Based Optimization. ICSIBO 2014. Lecture Notes in Computer Science(), vol 8472. Springer, Cham. https://doi.org/10.1007/978-3-319-12970-9_16
Download citation
Published:
Publisher Name:Springer, Cham
Print ISBN:978-3-319-12969-3
Online ISBN:978-3-319-12970-9
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative