关键词: 粒子群优化算法, 文化算法, 投资组合, 均值-风险价值（VaR）

Abstract: Portfolio decisions faces a great deal of data of the real security market, which is a complicated combinatorial optimization problem, and is a NP-hard problem, which is difficult to be solved by traditional algorithm. Cultural algorithm and particle swarm optimization are emerging bionic intelligence algorithms. This paper introduces a new dynamic particle swarm optimization based on cultural algorithm for solving the mean-VaR model, and uses the penalty function approach to the inequality constraints in the model. An empirical analysis is done by sixteen securities chosen from Shanghai and Shenzhen security markets as the alternative securities. The numerical results show that the problem of portfolio optimization can be solved more reasonably and efficiently by this algorithm.

Key words: particle swarm optimization, cultural algorithm, portfolio, mean-Value at Risk（VaR）