改进鱼群算法求解基数约束型投资组合问题

Improved fish swarm algorithm and its application in cardinality constrained portfolio optimization problem

运用群智能算法解决投资组合优化时会遇到收敛速度慢、收敛精度不高、鲁棒性弱等问题。鉴于此,对以往算法的不足进行研究,尝试运用规范化、随机选择行为中加入变异因子以及改进觅食行为的搜索策略,通过轮盘赌对整个鱼群进行择优选取并迭代。验证算法性能的数据集来自沪深股市中随机选取的20只股票的实时交易数据,对模型参数以及算法参数进行详细分析,分析结果表明,该算法具有较好性能。

Most swarm intelligence algorithms have typical issues such as slow convergence speed,low convergence accuracy and weak robustness when applied to portfolio optimization problems.To solve these problems,an improved artificial fish swarm algorithm（IAFSA）for portfolio optimization problems was proposed.The methods of standardization,putting variable factors into random selection behavior and improving the search strategy of foraging behavior were adopted,and better fishes were selected into the next generation using roulette based on the analysis of the deficiency of the previous algorithms.To verify the performance of proposed algorithm,the experimental data including 20 stocks from Shanghai and Shenzhen stock markets were randomly extracted from real-time trading datasets.By analyzing parameters in both the portfolio models and the algorithm in details,experimental results verify the better performance of the proposed algorithm.