Multiobjective extremal optimization for portfolio optimization problem

Portfolio optimization plays a critical role in determining portfolio strategies for investors and it is intrinsically a discrete multiobjective optimization problem whose decision criteria conflict with each other. This paper extends a novel numerical multiobjective optimization algorithm, so-called multiobjective extremal optimization (MOEO), to solve the portfolio optimization problem. The proposed approach is validated by five popular stock indexes. The simulation results indicate that the proposed approach is highly competitive with three state-of-the-art multiobjective evolutionary algorithms, i.e., NSGA-II, SPEA2 and PAES. Thus, MOEO can be considered a good alternative to solve portfolio optimization problem.