| 摘要: |
| 针对原始量子粒子群优化算法(QPSO)在面对复杂多模函数时容易出现早熟和收敛精度低的 情况,提出了一种具有随机扰动机制的改进 QPSO 算法(MQPSO)。在改进算法设计时,首先借鉴了遗传算 法中交叉算子的思想,并结合随机扰动操作,对单个粒子的历史最优位置和全局最优位置进行了重新设定, 以增强算法在迭代后期的收敛性能,同时维持种群的多样性;其次,对QPSO算法中的重要参数收缩-扩张因 子,进行了非线性调整,以提高算法的全局收敛速度和精度。 通过8个测试函数,将 MQPSO 算法与4个现有的改进算法从平均值、标准差和最好取值三个方面进行了对比;进而根据中国证券市场中 15 只股票的历史 数据,分别运用粒子群优化算法、量子粒子群优化算法、布谷鸟搜索、蝙蝠算法和 MQPSO 算法对一类具有最小最大风险的投资组合优化模型进行数值求解。实验表明:MQPSO算法无论在基准测试中还是在仿真应用上,其计算结果在收敛精度和稳定性方面均优于其他群智能算法。 |
| 关键词: 量子粒子群优化算法 随机扰动 收敛精度 最小最大型风险 |
| DOI： |
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| Improved QPSO Algorithm with Random Disturbance Mechanism and Its Application |
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HE Guang, LU Xiao-li1,2
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1. School of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067, China;2. Research Center for Economy of Upper Reaches of Yangtze River, Chongqing Technology and Business University,Chongqing 400067, China
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| Abstract: |
| In view of the situation that original quantum particle swarm optimization ( QPSO) is prone to premature and has poor convergence accuracy in complex multimode functions, an improved QPSO algorithm with random disturbance mechanism (MQPSO) is proposed. In the design of the improved algorithm, the idea of cross operator in genetic algorithm and random disturbance operation are applied to locate each particle’ s history best position and global optimal position for enhancing algorithm convergence ability in later iterations and maintaining the diversity of population. Moreover, to improve algorithm ’ s global convergence rate and accuracy, the contraction-expansion factor, an important parameter in QPSO algorithm is adjusted nonlinearly. Through eight test functions, MQPSO is compared with four exiting improved algorithms in three aspects including mean, standard deviation and best value. Based on history data of 15 stocks from Chinese security market, particle swarm optimization algorithm, QPSO, cuckoo search, bat algorithm and MQPSO are used to solve portfolio optimization models with minimax type risk respectively. Experiments indicate that whether in benchmarking or in simulation application, MQPSO is better than other swarm intelligence algorithms at convergence accuracy and stability. |
| Key words: quantum particle swarm optimization random disturbance convergence accuracy minimax type risk |
