Abstract
The rising popularity of multi-source, multi-sensor networks supports real-life applications calls for an efficient and intelligent approach to information fusion. Traditional optimization techniques often fail to meet the demands. The evolutionary approach provides a valuable alternative due to its inherent parallel nature and its ability to deal with difficult problems. We present a new evolutionary approach based on a modified version of Differential Evolution (DE), called Fitness Adaptive Differential Evolution (FiADE). FiADE treats sensors in the network as distributed intelligent agents with various degrees of autonomy. Existing approaches based on intelligent agents cannot completely answer the question of how their agents could coordinate their decisions in a complex environment. The proposed approach is formulated to produce good result for the problems that are high-dimensional, highly nonlinear, and random. The proposed approach gives better result in case of optimal allocation of sensors. The performance of the proposed approach is compared with an evolutionary algorithm coordination generalized particle model (C-GPM).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Forrest, S.: Genetic algorithms—principles of natural-selection applied to computation. Science 261(5123), 872–878 (1993)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Inspiration for optimization from social insect behaviour. Nature 406(6791), 39–42 (2000)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. IEEE Conf. Neural Networks, Piscataway, NJ, vol. IV, pp. 1942–1948 (1995)
Price, K., Storn, R., Lampinen, J.: Differential Evolution - A Practical Approach to Global Optimization. Springer, Berlin (2005)
Storn, R., Price, K.: Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)
Liu, J., Lampinen, J.: On setting the control parameters of the differential evolution method. In: Matoušek, R., Ošmera, P. (eds.) Proc. of Mendel 2002, 8th International Conference on Soft Computing, pp. 11–18 (2002)
Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization”. IEEE Transactions on Evolutionary Computation 13(2), 398–417 (2009)
Brest, J., Greiner, S., Bošković, B., Mernik, M., Žumer, V.: Self-adapting Control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Transaction on Evolutionary Computation 10(6), 646–657 (2006)
Feng, X., Lau, F.C.M., Shuai, D.: The Coordination generalized Particle Model-An evolutionary approach to multi sensor fusion. Information Fusion 9(4), 450–464 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Giri, R., Ghosh, A., Chowdhury, A., Das, S. (2010). Multi Sensor Fusion Using Fitness Adaptive Differential Evolution. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-17563-3_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17562-6
Online ISBN: 978-3-642-17563-3
eBook Packages: Computer ScienceComputer Science (R0)