Abstract
In this paper we tackle the standard Markowitz mean-variance model extended to include complex constraints. We formulate the problem as a bi-objective mixed integer optimization problem, i.e. maximization of return and minimization of risk. Τo find the set of Pareto-optimal portfolios, we implement two multiobjective algorithms, a population based multiobjective optimizer and a multiobjective optimizer which uses a local search evolution strategy. Finally, we evaluate the performance of the two multiobjective evolutionary algorithms on a public benchmark data set and a data set constructed using a representative emerging market’s index.
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