Abstract
Although numbers of heuristic algorithms are successfully developed for solving portfolio optimization problems, this is not for all cases of the large-scale ones. A large-scale portfolio optimization involves dealing with the large search space and dense variance-covariance matrix associated with the problem. This paper proposed a new multi-objective algorithm for solving a large-scale optimization problem based upon the notion of cooperative coevolutionary algorithms (CCA). The new problem decomposition scheme was designed by allowing the species-size to be dynamically adjusted as the runs progress. This scheme enhances capability of traditional CCA in dealing with non-separable optimization problem. The collaborator selection method was modified to allow the proposed CCA to perform in a multi-objective (MO) optimization framework. Additionally, the proposed algorithm, named as “DMOCCA”, was implemented for solving large-scale portfolio optimization problem with cardinality constraint using the real-world data set having scale up to 2196 dimensions. Moreover, its performances were benchmarked with those of the SPEA-II and MOPSO.
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Notes
- 1.
It should be noted that the data source employed provides the true Pareto front for given values of cardinality. However, these values are restricted for only K = 2, 3, 4, and 5. To conserve space, only results of K = 2 and K = 5 are reported.
References
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
Liu, Y., Yao, X., Zhao, Q., Higuchi, T.: Scaling up fast evolutionary programming with cooperative coevolution. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), vol. 1102, pp. 1101–1108 (2001)
van den Bergh, F., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8, 225–239 (2004)
Shi, Y., Teng, H., Li, Z.: Cooperative co-evolutionary differential evolution for function optimization. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 1080–1088. Springer, Heidelberg (2005). https://doi.org/10.1007/11539117_147
Yang, Z., Tang, K., Yao, X.: Large scale evolutionary optimization using cooperative coevolution. Inf. Sci. 178, 2985–2999 (2008)
Iorio, A.W., Li, X.: A cooperative coevolutionary multiobjective algorithm using non-dominated sorting. In: Deb, K. (ed.) GECCO 2004. LNCS, vol. 3102, pp. 537–548. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24854-5_56
Yang, Z., Tang, K., Yao, X.: Multilevel cooperative coevolution for large scale optimization. In: IEEE Congress on Evolutionary Computation, 2008, CEC 2008, (IEEE World Congress on Computational Intelligence), pp. 1663–1670 (2008)
Omidvar, M.N., Li, X., Yang, Z., Yao, X.: Cooperative co-evolution for large scale optimization through more frequent random grouping. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2010)
Li, X., Yao, X.: Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans. Evol. Comput. 16, 210–224 (2012)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA-II: Improving the Strength Pareto Evolutionary Algorithm. Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (2002)
Coello, C.A.C., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8, 256–279 (2004)
Deb, K., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization. In: ICGA, pp. 416–423 (1993)
Markowitz, H.: Portfolio selection. J. Finan. 7, 77–91 (1952)
Deb, K., Beyer, H.-G.: Self-adaptive genetic algorithms with simulated binary crossover. Evol. Comput. 9, 197–221 (2001)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8, 173–195 (2000)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Evol. Comput. 3, 257–271 (1999)
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Suksonghong, K., Boonlong, K. (2018). Multi-objective Cooperative Coevolutionary Algorithm with Dynamic Species-Size Strategy. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_1
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