Abstract
This paper presents a coevolutionary memetic particle swarm optimizer (CMPSO) for the global optimization of numerical functions. CMPSO simplifies the update rules of the global evolution and utilizes five different effective local search strategies for individual improvement. The combination of the local search strategy and its corresponding computational budget is defined as coevolutionary meme (CM). CMPSO co-evolves both CMs and a single particle position recording the historical best solution that is optimized by the CMs in each generation. The experimental results on 7 unimodal and 22 multimodal benchmark functions demonstrate that CMPSO obtains better performance than other representative state-of-the-art PSO variances. Particularly, CMPSO is shown to have higher convergence speed.
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 Optimization. In: IEEE International Conference on Neural Network, Australia, pp. 1942–1948 (1995)
Liang, J.J., Qin, A.K., Suganthan, P.N., et al.: Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions. IEEE Transactions on Evolutionary Computation 10(3), 281–295 (2006)
Zhan, Z.H., Zhang, J., Li, Y., et al.: Orthogonal Learning Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation 15(6), 832–847 (2010)
De Oca, M.A.M., Aydin, D., Stützle, T.: An Incremental Particle Swarm for Large-Scale Optimization Problems: An Example of Tuning-in-the-loop (Re)Design of Optimization Algorithms. Soft Computing 15, 2233–2255 (2011)
Yang, Z.Y., Tang, K., Yao, X.: Scalability of Generalized Adaptive Differential Evolution for Large-Scale Continuous Optimization. Soft Computing 15, 2141–2155 (2011)
Davidon, W.: Variable Metric Method for Minimization. SIAM Journal on Optimization 1(1), 1–17 (1991)
Schwefel, H.P.: Evolution and Optimum Seeking: the Sixth Generation. John Wiley & Sons, USA (1993)
Gao, Y., Xie, S.L.: Chaos Particle Swarm Optimization Algorithm. Computer Science 31(8), 13–15 (2004)
Enriquez, N., Sabot, C.: Random Walks in a Dirichlet Environment. Electronic Journal of Probability 11(31), 802–817 (2006)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)
Nguyen, Q.H., Ong, Y.S., Lim, M.H.: Non-genetic Transmission of Memes by Diffusion. In: Annual Conference on Genetic and Evolutionary Computation, USA, pp. 1017–1024 (2008)
Dawkins, R.: The Selfish Gene, 2nd edn. Oxford University Press, UK (1989)
Yao, X., Liu, Y., Lin, G.M.: Evolutionary Programming Made Faster. IEEE Transactions on Evolutionary Computation 3(2), 82–102 (1999)
Liang, J.J., Suganthan, P.N., Deb, K.: Novel Composition Test Functions for Numerical Global Optimization. In: IEEE Swarm Intelligence Symposium, USA, pp. 68–75 (2005)
Suganthan, P.N., Hansen, N., Liang, J.J., et al.: Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-parameter Optimization. In: IEEE Congress on Evolutionary Computation, UK (2005)
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
Zhou, J., Ji, Z., Zhu, Z., Chen, S. (2012). A Coevolutionary Memetic Particle Swarm Optimizer. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-30976-2_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30975-5
Online ISBN: 978-3-642-30976-2
eBook Packages: Computer ScienceComputer Science (R0)