Abstract
A velocity-free multi-objective particle swarm optimizer with centroid is proposed and applied to optimization of wireless sensor network. Different from the standard PSO, particles in swarm only have position without velocity in the algorithm. Besides, not only the personal best position and the global best position but also the centroid is considered to update the particle position. The initial swarm is generated using the opposition-based learning, and an archive with maximum capacity is used to maintain the non-dominated solutions. The global best solution is selected from the archive on the basis of the diversity of the solutions, and the crowding-distance measure is used for the diversity measurement. The archive gets updated with the inclusion of the non-dominated solutions from the combined population of the swarm and current archive, and the archive which exceeds the maximum capacity is cut using the diversity consideration. The proposed algorithm is applied to some well-known benchmark and optimization of wireless sensor network by maximizing network coverage and lifetime. The relative experimental results show that the algorithm has better performance and is effective.
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.
Similar content being viewed by others
References
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6(2), 182–197 (2002)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948 (1995)
Mostaghim, S., Teich, J.: Strategies for Finding Good Local Guides in Multi-objective Particle Swarm Optimization (SIS 2003), pp. 26–33. IEEE Service Center, Inidanapolis (2003)
Coello, C.A.C., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Trans. on Evolutionary Computation 8(3), 256–279 (2004)
Ngatchou, P., Fox, W., Sharkawi, M.: Distributed sensor placement with sequential particle swarm optimization. In: Proc. IEEE Swarm Intelligence Symp., pp. 385–388 (June 2005)
Seah, M., Tham, C., Srinivasan, K., Xin, A.: Achieving coverage through distributed reinforcement learning in wireless sensor networks. In: Proc. 3rd Int. Conf. Intelligent Sensors, Sensor Network. Inf. Proc. (2007)
Gao, Y.: No Velocity Particle Swarm Optimiser with Forgetting Factor and Center. In: ICNC 2009-FSKD 2009, pp. 537–541 (August 2009)
Tizhoosh, H.R.: Opposition-Based Learning: A New Scheme for Machine Intelligence. In: Int. Conf. on Computational Intelligence for Modelling Control and Automation, Vienna, Austria, vol. I, pp. 695–701 (2005)
Cormen, T.H., et al.: Introduction to algorithms. MIT Press, Cambridge (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gao, Y., Peng, L., Li, F., MiaoLiu, Hu, X. (2012). Velocity-Free Multi-Objective Particle Swarm Optimizer with Centroid for Wireless Sensor Network Optimization. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Artificial Intelligence and Computational Intelligence. AICI 2012. Lecture Notes in Computer Science(), vol 7530. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33478-8_84
Download citation
DOI: https://doi.org/10.1007/978-3-642-33478-8_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33477-1
Online ISBN: 978-3-642-33478-8
eBook Packages: Computer ScienceComputer Science (R0)