Abstract
This paper attempts to address the problem of large scale optimization and high dimensional optimization using principal component analysis (PCA) strategy with differential evolution (DE) based on Cooperative Co-evolution (CC) framework. Decomposition problem is a major obstacle for large-scale optimization problems. The aim of this paper is to propose effective dimension decomposition method of PCA strategy for capturing the main information among dimensions. PCA strategy can measures most of the contribution information of dimension and uses it for identifying main dimension to guide them to group the most promising subcomponents in CC framework. Then each subcomponents can be solved using an evolutionary optimizer to find the optimum values. The experimental results show that this new technique is more effective than some existing grouping methods.
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
References
Kazimipour, B., Salehi, B., Jahromi, M.Z.: A novel genetic-based instance selection method: Using a divide and conquer approach. In: 2012 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), pp. 397–402. IEEE (2012)
Qin, A.K., Raimondo, F., Forbes, F., Ong, Y.S.: An improved CUDA-based implementation of differential evolution on GPU. In: the 2012 Conference on Genetic and Evolutionary Computation, pp. 991–998 (2012)
Peng, H., Wu, Z.: Heterozygous differential evolution with Taguchi local search. Soft Comput. 19(11), 3273–3291 (2015)
Mahdavi, S., Shiri, M.E., Rahnamayan, S.: Metaheuristics in largescale global continues optimization: a survey. Inf. Sci. 395, 407–428 (2014)
Kazimipour, B., Li, X., Qin, A.K.: Why advanced population initialization techniques perform poorly in high dimension? In: Dick, G., et al. (eds.) SEAL 2014. LNCS, vol. 8886, pp. 479–490. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13563-2_41
Vicini, A., Quagliarella, D.: Airfoil and wing design through hybrid optimization strategies. AIAA J 37, 634–641 (1999)
Potter, M.A., De Jong, K.A.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Schwefel, H.-P., Männer, R. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994). https://doi.org/10.1007/3-540-58484-6_269
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Network, vol. 4, pp. 1942–1948 (1995)
Yang, Z., Tang, K., Yao, X.: Large scale evolutionary optimization using cooperative coevolution. Inf. Sci. 178, 2986–2999 (2008)
Omidvar, M.N., Li, X., Yao, X.: Cooperative co-evolution with delta grouping for large scale non-separable function optimization. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 1762–1769, July 2010
Chen, W., Weise, T., Yang, Z., Tang, K.: Large-scale global optimization using cooperative coevolution with variable interaction learning. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN 2010 Part II. LNCS, vol. 6239, pp. 300–309. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15871-1_31
Omidvar, M.N., Li, X., Mei, Y., et al.: Cooperative co-evolution with differential grouping for large scale optimization. IEEE Trans. Evol. Comput. 18(3), 378–393 (2014)
Xie, J., Chen, W., Zhang, D., et al.: Application of principal component analysis in weighted stacking of seismic data. IEEE Geosci. Remote Sens. Lett. 14(8), 1213–1217 (2017)
Chu, W., Gao, X.G., Sorooshian, S.: Fortify particle swam optimizer (PSO) with principal components analysis. In: 2011 IEEE Congress on Evolutionary Computation, pp. 1644–1648 (2011)
Kuznetsova, A., Pons-Moll, G., Rosenhahn, B.: PCA-enhanced stochastic optimization methods. In: Pinz, A., Pock, T., Bischof, H., Leberl, F. (eds.) DAGM/OAGM 2012. LNCS, vol. 7476, pp. 377–386. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32717-9_38
Yang, Z., Tang, K., Yao, X.: Self-adaptive differential evolution with neighborhood search. In: Evolutionary Computation, pp. 1110–1116. IEEE (2008)
Tang, K., Li, X., Suganthan, P.N., Yang, Z., Weise, T.: Benchmark functions for the CEC 2010 special session and competition on large-scale global optimization. Nature Inspired Computation and Applications Laboratory (2009)
Zhang, S.X., Zheng, S.Y., Zheng, L.M.: An efficient multiple variants coordination framework for differential evolution. IEEE Trans. Cybern. 47(9), 2780–2793 (2017)
Yang, Z., Tang, K., Yao, X.: Multilevel cooperative coevolution for large scale optimization. In: IEEE Congress on Computational Intelligence, pp. 1663–1670. IEEE (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xu, G., Zhao, X., Li, R. (2018). Cooperative Co-evolution with Principal Component Analysis for Large Scale Optimization. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 952. Springer, Singapore. https://doi.org/10.1007/978-981-13-2829-9_39
Download citation
DOI: https://doi.org/10.1007/978-981-13-2829-9_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2828-2
Online ISBN: 978-981-13-2829-9
eBook Packages: Computer ScienceComputer Science (R0)