Abstract
The energy-efficiency coverage of wireless sensor network is measure by the network cover rate and the node redundancy rate. To solve this multi-objective optimization problem, a multi-objective quantum-inspired cultural algorithm is proposed, which adopts the dual structure to effectively utilize the implicit knowledge extracted from the non-dominating individuals set to promote more efficient search. It has three highlights. One is the rectangle’s height of each allele is calculated by non-dominated sort among individuals. The second is the crowding degree that records the density of non-dominated individuals in the topological cell measure the uniformity of the Pareto-optimal set instead of the crowding distance. The third is the update operation of quantum individuals and the selection operator are directed by the knowledge. Simulation results indicate that the layout of wireless sensor network obtained by this algorithm have larger network cover rate and less node redundancy rate.
This is a preview of subscription content, log in via an institution to check access.
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
Jia, J., Chen, J., Chang, G.-R., et al.: Optimal coverage scheme based on genetic algorithm in wireless sensor networks. Control and Decision 22(11), 1289–1292 (2007)
Lee, J.-W., Choi, B.-S., Lee, J.-J.: Energy-efficient coverage of wireless sensor networks using ant colony optimization with three types of pheromones. IEEE Transactions on Industrial Informatics 7(3), 419–427 (2011)
Aziz, N.A., Mohemmed, A.W., Alias, Y.: A wireless sensor network cverageoptimization algorithm based on particle swarm optimization and voronoidiagram. In: IEEE International Conference on Networking, Sensing and Control, pp. 602–607 (2009)
Hua, F., Shuang, H.: Optimal sensor node distribution based on the new quantum genetic algorithm. Chinese Journal of Sensors and Actuators 21(7), 1259–1263 (2008)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm,Technical Report 103, Computer Engineering andNetworks Laboratory, Swiss Federal Institute of Technology Zurich, Switzerland (2001)
Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithmfor multiobjective optimization. In: IEEE World Congress on Computational Intelligence, pp. 67–72 (1994)
Deb, K.: A fast and elitist multiobjective geneticalgorithm: NSGA-II. IEEE Transaction on EvolutionaryComputation 6(2), 182–197 (2002)
Meshoul, S., Mahdi, K., Batouche, M.: A quantum inspired evolutionary framework for multi-objective optimization. In: Bento, C., Cardoso, A., Dias, G. (eds.) EPIA 2005. LNCS (LNAI), vol. 3808, pp. 190–201. Springer, Heidelberg (2005)
Kim, Y., Kim, J.-H., Han, K.-H.: Quantum-inspired multiobjectiveevolutionary algorithm formultiobjective 0/1 knapsack problems. In: 2006 IEEE Congress on Evolutionary Computation, pp. 9151–9156 (2006)
Wei, X., Fujimura, S.: Multi-objective quantum evolutionary algorithm for discrete multi-objective combinational problem. In: Proceeding of International Conference on Technologies and Applications of Artificial Intelligence, pp. 39–46 (2010)
Yang, X.-W., Shi, Y.: A real-coded quantum clone multi-objective evolutionary algorithm. In: Proceeding of International Conference on Consumer Electronic, Communications and Networks, pp. 4683–4687 (2011)
Reynolds, R.G.: An introduction to cultural algorithms. In: Proceeding of the Third Annual Conference on Evolutionary Programming, pp. 131–139 (1994)
Guo, Y.-N., Liu, D., Cheng, J., et al.: A novel real-coded quantum-inspired cultural algorithm. Journal of Central South University 42, 130–136 (2011)
Li, S.J., Xu, C.F., Pan, Y.H.: Sensor deployment optimization for detecting maneuvering targets. In: Proceedings of International Conference on Information Fusion, pp. 1629–1635 (2005)
Cruz, A.V.A., Vellasco, M.B.R., Pacheco, M.A.C.: Quantum-inspired evolutionary algorithm for numerical optimization. In: Proceeding of IEEE Congress on Evolutionary Computation, pp. 19–37 (2006)
Best, C., Che, X., Reynolds, R.G., et al.: Multi-objective cultural algorithms. In: Proceeding of IEEE Congress on Evolutionary Computation, pp. 1–9 (2010)
Bandyopadhyay, S., Pal, S.K., Aruna, B.: Multiobjective GAs,quantitative indices and pattern classification. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 5(34), 2088–2099 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, Y., Liu, D., Chen, M., Liu, Y. (2013). An Energy-Efficient Coverage Optimization Method for the Wireless Sensor Networks Based on Multi-objective Quantum-Inspired Cultural Algorithm. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-39065-4_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39064-7
Online ISBN: 978-3-642-39065-4
eBook Packages: Computer ScienceComputer Science (R0)