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Hybrid Imperialist Competitive Evolutionary Algorithm for Solving Biobjective Portfolio Problem

Chun’an Liu1,*, Qian Lei2, Huamin Jia3

1 Department of Mathematics, Baoji University of Arts and Sciences, Baoji, 721013, China
2 Center for Big Data Research in Languages/School of English Studies, Xi’an International Studies University, Xi’an, 710071, China
3 School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, MK43 0AL, UK

* Corresponding Author: Chun’an Liu. Email:

Intelligent Automation & Soft Computing 2020, 26(6), 1477-1492.


Portfolio optimization is an effective way to diversify investment risk and optimize asset management. Many multiobjective optimization mathematical models and metaheuristic intelligent algorithms have been proposed to solve portfolio problem under an ideal condition. This paper presents a biobjective portfolio optimization model under the assumption of no short selling. In order to obtain sufficient number of portfolio optimal solutions uniformly distributed on the portfolio efficient Pareto front, a hybrid imperialist competitive evolutionary algorithm which combines a multi-colony levy crossover operator and a simple-colony moving operator with random perturbation is also given. The performance of the given algorithm is verified by four criterion portfolio test problems, and the simulation results and comparison analyses illustrate that the proposed algorithm could obtain faster convergence toward the portfolio true Pareto front compared with the other two state of the art multiobjective optimization methods. The results can provide optimal portfolio plans and investment strategies for investors to allocate and manage assets effectively.


Cite This Article

C. Liu, Q. Lei and H. Jia, "Hybrid imperialist competitive evolutionary algorithm for solving biobjective portfolio problem," Intelligent Automation & Soft Computing, vol. 26, no.6, pp. 1477–1492, 2020.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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