Abstract
Due to the nature of multiple objectives, the portfolio optimization problems based on the Mean-Variance model are very suitable to be solved by multi-objective evolutionary algorithms (MOEAs). However, most of the existing MOEAs do not consider the preferences of decision-makers. Meanwhile, there are certain limitations to the traditional crossover and mutation strategy in the exploration of population diversity. To overcome these disadvantages, this paper proposes a multi-objective evolutionary algorithm based on piecewise parallel decomposition (MOEA-PPD). The algorithm takes the largest Sharpe ratio as the piecewise marker of parallel decomposition for portfolio optimization problems. In addition, a neuro-evolution strategy is applied to better approximate a global non-dominated front in the early stage. Then a crossover and mutation strategy is used to enhance the convergence of the obtained non-dominated front in the later stage. Experimental results show that the proposed algorithm is competitive in terms of diversity and convergence for portfolio optimization.
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References
Fahimi, A., Shahbandarzadeh, H.: Developing the Markowitz portfolio optimization model concerning investor non-financial considerations and supporting domestic products. Ind. Manage. J. 13(1), 53–79 (2021)
Markowitz, H.: Portfolio selection. J. Finance 7(1), 77–91 (1952)
Jaber, E.A., Miller, E., Pham, H.: Markowitz portfolio selection for multivariate affine and quadratic volterra models. SIAM J. Finan. Math. 12(1), 369–409 (2021)
Sharpe, W.F.: Mutual fund performance. J. Bus. 39(1), 119–138 (1966)
Ming, G., Hui, O.-Y.: Alpha decay and Sharpe ratio: two measures of investor performance. Econ. Modell. 104, 105558 (2021)
Jiang, R.W., Tang, J.: Multi-objective evolutionary algorithm based on data analysis and its application in portfolio optimization. In: Jan, M.A., Khan, F. (eds.) BigIoT-EDU 2021. LNICST, vol. 392, pp. 454–458. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87903-7_55
Zhao, P., Gao, S., Yang, N.: Solving multi-objective portfolio optimization problem based on MOEA/D. In: International Conference on Advanced Computational Intelligence, pp. 30–37. IEEE (2020)
Ertenlice, O., Kalayci, C.B.: A survey of swarm intelligence for portfolio optimization: algorithms and applications. Swarm Evol. Comput. 39, 36–52 (2018)
Zhang, Q., Li, H., Maringer, D., Tsang,E.: MOEA/D with NBI-style Tchebycheff approach for portfolio management. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2010)
Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
Hans Georg Beyer and Hans Paul Schwefel: Evolution strategies - a comprehensive introduction. Natural Comput. 1(1), 3–52 (2002)
Denysiuk,R., Gaspar-Cunha, A., Delbem, A.C.B.: Neuroevolution for solving multiobjective knapsack problems. Expert Syst. Appl. 116, 65–77 (2019)
Chang, T.-J., Meade, N., Beasley, J.E., Sharaiha, Y.M.: Heuristics for cardinality constrained portfolio optimisation. Comput. Oper. Res. 27(13), 1271–1302 (2000)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)
Ishibuchi, H., Masuda, H., Tanigaki, Y., Nojima, Y.: Modified distance calculation in generational distance and inverted generational distance. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C.C. (eds.) EMO 2015. LNCS, vol. 9019, pp. 110–125. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15892-1_8
Acknowledgments
This work was supported in part by the Natural Science Foundation of Guangdong Province, China, under Grant 2020A1515011491 and Grant 2019A1515011792, in part by the Science Research Project of Guangzhou University under Grant YG2020008, in part by the Project of Innovation and Developing Universities of Education Department of Guangdong Province under Grant 2019KTSCX130, in part by the Guangzhou Science and Technology Project under Grant 202102080161, in part by the Guangdong Science and Technology Department, Grant 2019B010154004, and in part by the Fundamental Research Funds for the Central Universities, SCUT, under Grant 2017MS043.
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Wu, Y., Wei, J., Ying, W., Cui, Z., Pan, X., Wang, Z. (2022). A Multi-objective Evolutionary Algorithm Based on Piecewise Parallel Decomposition for Portfolio Optimization. In: Li, K., Liu, Y., Wang, W. (eds) Exploration of Novel Intelligent Optimization Algorithms. ISICA 2021. Communications in Computer and Information Science, vol 1590. Springer, Singapore. https://doi.org/10.1007/978-981-19-4109-2_5
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DOI: https://doi.org/10.1007/978-981-19-4109-2_5
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