Abstract
In this paper, a new algorithm for particle swarm optimisation (PSO) is proposed. In this algorithm, the particles are divided into two groups. The two groups have different focuses when all the particles are searching the problem space. The first group of particles will search the area around the best experience of their neighbours. The particles in the second group are influenced by the best experience of their neighbors and the individual best experience, which is the same as the standard PSO. Simulation results and comparisons with the standard PSO 2007 demonstrate that the proposed algorithm effectively enhances searching efficiency and improves the quality of searching.
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
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimisation. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948 (1995)
Hu, X., Shi, Y., Eberhart, R.: Recent Advances in Particle Swarm. In: Congress on Evolutionary Computation, pp. 90–97. IEEE Service Center, Piscataway (2004)
Huang, C.M., Huang, C.J., Wang, M.L.: A Particle Swarm Optimisation to Identifying the ARMAX Model for Short term Load Forecasting. IEEE Transactions on Power Systems 20, 1126–1133 (2005)
Clerc, M.: Particle Swarm Optimisation. ISTE Publishing Company (2006)
Nedjah, N., Mourelle, L.D.M.: Systems Engineering Using Particle Swarm Optimisation. Nova Science Publishers (2007)
Aihara, K., Takabe, T., Toyoda, M.: Chaotic Neural Networks. Physics Letter A 144(6-7), 333–340 (1990)
Shi, Y., Eberhart, R.: A Modified Particle Swarm Optimiser. In: IEEE International Conference on Evolutionary Computation, pp. 69–73. IEEE Press, Piscataway (1998)
Zhang, W.J., Xie, X.F.: DEPSO: Hybrid Particle Swarm with Differential Evolution Operator. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Washington DC, USA, pp. 3816–3821 (2003)
Mohagheghi, S., Del Valle, Y., Venayagamoorthy, G., Harley, R.: A Comparison of PSO and Back Propagation for Training RBF Neural Networks for Identification of a Power System with STATCO. In: Proceedings of IEEE Swarm Intelligence Symposium, pp. 381–384 (June 2005)
del Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J., Harley, R.G.: Particle Swarm Optimisation: Basic Concepts, Variants and Applications in Power Systems. IEEE Transactions On Evolutionary Computation 12(2), 171–195 (2008)
Doctor, S., Venayagamoorthy, G., Gudise, V.: Optimal PSO for Collective Robotic sSearch Applications. In: Proceeding IEEE Congress on Evolutionary Computation, Portland, Oregon, USA, pp. 1390–1395 (2004)
Venayagamoorthy, G.: Adaptive Critics for Dynamic Particle Swarm Optimisation. In: Proceedings of IEEE International Symposium on Intelligence Control, Taipei, Taiwan, pp. 380–384 (September 2004)
Liu, B., Wang, L., Jin, Y.H., Tang, F., Huang, D.X.: Improved Particle Swarm Optimisation Combined with chaos. Chaos, Solitons and Fractals 25, 1261–1271 (2005)
Kennedy, J., Clerc, M., et al.: Particle Swarm Central (2012), http://www.particleswarm.info/Programs.html
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
Sun, Y., Wang, Z., van Wyk, B.J. (2013). Local and Global Search Based PSO Algorithm. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38703-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-38703-6_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38702-9
Online ISBN: 978-3-642-38703-6
eBook Packages: Computer ScienceComputer Science (R0)