Abstract
Cooperative co-evolution (CC) is an effective framework for evolutionary algorithms (EAs) to solve large-scale optimization problems. By combining a divide-and-conquer strategy and the classic evolutionary algorithms (EA) like genetic algorithm (GA), CC has shown promising performance in many fields. As a family of EAs, the estimation of distribution algorithm (EDA) is good at search diversity maintenance, but its capability in solving large-scale problems has not been fully explored. In this paper, we aim to propose a new estimation of distribution algorithm with the cooperative co-evolution framework (EDACC). The proposed EDACC has the following features. (1) The differential grouping (DG) strategy is applied for variable decomposition. (2) A combination of the Gaussian and Cauchy distributions are adopted to generate offspring. (3) A local search method is performed in promising domains to accelerate the search. To verify the performance of EDACC, experiments are conducted on 20 single-objective functions in the CEC 2010 benchmarks. The experimental results show that EDACC can still achieve competitive performance in spite of the weakness of the original EDAs like the low accuracy in global optima searching compared with classical EAs.
This work was supported in part by the National Natural Science Foundation of China under Grant 61622206, 61332002, and the Natural Science Foundation of Guangdong under Grant 2015A030306024.
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
Antonio, L.M., Coello, C.A.C.: Use of cooperative coevolution for solving large scale multiobjective optimization problems. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 2758–2765. IEEE (2013)
Bengoetxea, E., Larrañaga, P., Bloch, I., Perchant, A.: Estimation of distribution algorithms: a new evolutionary computation approach for graph matching problems. In: Figueiredo, M., Zerubia, J., Jain, A.K. (eds.) EMMCVPR 2001. LNCS, vol. 2134, pp. 454–469. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44745-8_30
Van den Bergh, F., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 225–239 (2004)
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. LNCS, vol. 6239, pp. 300–309. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15871-1_31
Cheng, R., Jin, Y.: A competitive swarm optimizer for large scale optimization. IEEE Trans. Cybern. 45(2), 191–204 (2015)
Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)
Fan, J., Wang, J., Han, M.: Cooperative coevolution for large-scale optimization based on kernel fuzzy clustering and variable trust region methods. IEEE Trans. Fuzzy Syst. 22(4), 829–839 (2014)
Fletcher, R.: Practical Methods of Optimization. Wiley, Hoboken (2013)
Hauschild, M., Pelikan, M.: An introduction and survey of estimation of distribution algorithms. Swarm Evol. Comput. 1(3), 111–128 (2011)
Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, Boston (2011)
Luo, N., Qian, F.: Estimation of distribution algorithm sampling under Gaussian and Cauchy distribution in continuous domain. In: 2010 8th IEEE International Conference on Control and Automation (ICCA), pp. 1716–1720. IEEE (2010)
Mühlenbein, H., Paaß, G.: From recombination of genes to the estimation of distributions I. Binary parameters. In: Voigt, H.-M., Ebeling, W., Rechenberg, I., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 178–187. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-61723-X_982
Omidvar, M.N., Li, X., Mei, Y., Yao, X.: Cooperative co-evolution with differential grouping for large scale optimization. IEEE Trans. Evol. Comput. 18(3), 378–393 (2014)
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
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes 3rd Edition: The Art of Scientific Computing. Cambridge University Press, New York (2007)
Santana, R., Larrañaga, P., Lozano, J.A.: Protein folding in simplified models with estimation of distribution algorithms. IEEE Trans. Evol. Comput. 12(4), 418–438 (2008)
Sebag, M., Ducoulombier, A.: Extending population-based incremental learning to continuous search spaces. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 418–427. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056884
Shi, W., Chen, W.N., Lin, Y., Gu, T., Kwong, S., Zhang, J.: An adaptive estimation of distribution algorithm for multi-policy insurance investment planning. IEEE Trans. Evol. Comput. (2017)
Srinivasa, K., Venugopal, K., Patnaik, L.M.: A self-adaptive migration model genetic algorithm for data mining applications. Inf. Sci. 177(20), 4295–4313 (2007)
Sun, Y., Kirley, M., Halgamuge, S.K.: Extended differential grouping for large scale global optimization with direct and indirect variable interactions. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 313–320. ACM (2015)
Teklu, F., Sumalee, A., Watling, D.: A genetic algorithm approach for optimizing traffic control signals considering routing. Comput. Aided Civ. Infrastruct. Eng. 22(1), 31–43 (2007)
Yang, Q., Chen, W.N., Li, Y., Chen, C.P., Xu, X.M., Zhang, J.: Multimodal estimation of distribution algorithms. IEEE Trans. Cybern. 47(3), 636–650 (2017)
Zhou, A., Sun, J., Zhang, Q.: An estimation of distribution algorithm with cheap and expensive local search methods. IEEE Trans. Evol. Comput. 19(6), 807–822 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Lin, JY., Chen, WN., Zhang, J. (2018). An Estimation of Distribution Algorithm for Large-Scale Optimization with Cooperative Co-evolution and Local Search. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-04179-3_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04178-6
Online ISBN: 978-3-030-04179-3
eBook Packages: Computer ScienceComputer Science (R0)