A hybrid algorithm for constrained portfolio selection problems


Since Markowitz’s seminal work on the mean-variance model in modern portfolio theory, many studies have been conducted on computational techniques and recently meta-heuristics for portfolio selection problems. In this work, we propose and investigate a new hybrid algorithm integrating the population based incremental learning and differential evolution algorithms for the portfolio selection problem. We consider the extended mean-variance model with practical trading constraints including the cardinality, floor and ceiling constraints. The proposed hybrid algorithm adopts a partially guided mutation and an elitist strategy to promote the quality of solution. The performance of the proposed hybrid algorithm has been evaluated on the extended benchmark datasets in the OR Library. The computational results demonstrate that the proposed hybrid algorithm is not only effective but also efficient in solving the mean-variance model with real world constraints.

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    In some literature, it is also known as quantity constraints or buy-in threshold constraints.


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This research was support by the School of Computer Science, The University of Nottingham.

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Correspondence to Khin Lwin.

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Lwin, K., Qu, R. A hybrid algorithm for constrained portfolio selection problems. Appl Intell 39, 251–266 (2013). https://doi.org/10.1007/s10489-012-0411-7

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  • Mean-variance portfolio optimization
  • Constrained portfolio selection problem
  • Cardinality constrained portfolio selection
  • Differential evolution
  • Population based incremental learning