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计算机系统应用:2019,28(2):171-176
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基于天牛须搜索的粒子群优化算法求解投资组合问题
陈婷婷1, 殷贺2, 江红莉2, 王露2
(1.江苏大学 理学院, 镇江 212013;2.江苏大学 财经学院, 镇江 212013)
Particle Swarm Optimization Algorithm Based on Beetle Antennae Search for Solving Portfolio Problem
CHEN Ting-Ting1, YIN He2, JIANG Hong-Li2, WANG Lu2
(1.School of Science, Jiangsu University, Zhenjiang 212013, China;2.School of Finance & Economics, Jiangsu University, Zhenjiang 212013, China)
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投稿时间:2018-08-09    修订日期:2018-09-05
中文摘要: 粒子群算法(PSO)作为一种群智能算法,有效提高了投资组合模型的实用性,但存在搜索精度较低和易陷入局部最优的缺陷.为克服其缺点,本文提出基于天牛须搜索(BAS)的粒子群优化算法(简称BSO),并将其应用到包含完整费用的投资组合模型中.在基于天牛须搜索的优化算法中(BSO),每个粒子的更新规则源自BAS,在每次迭代中都有自己对环境空间的判断,而不仅依赖于PSO中历史最佳解决方案和粒子个体的当前全局最优解,从而减少迭代次数、提高搜索速度和精度.实证结果表明算法更具稳定性和有效性.
Abstract:Particle Swarm Optimization (PSO), as a group intelligence algorithm, effectively improves the practicability of the portfolio model, but it has the disadvantages of low search accuracy and easy to fall into local optimum. In order to overcome its shortcomings, this study proposes a particle swarm optimization algorithm based on the Beetle Antennae Search (Abbreviated as BAS), and applies it to the portfolio model with full cost. In the Optimization algorithm based on BAS (BSO), the update rule of each particle is derived from BAS. In each iteration, it has its own judgment on the environment space, and not only depends on the historical best solution in the PSO and the current global optimal solution of the particle individual, thereby reducing the number of iterations, improving search speed and accuracy. The empirical results show that the algorithm is more stable and effective.
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基金项目:国家自然科学基金面上项目(71671037)
引用文本:
陈婷婷,殷贺,江红莉,王露.基于天牛须搜索的粒子群优化算法求解投资组合问题.计算机系统应用,2019,28(2):171-176
CHEN Ting-Ting,YIN He,JIANG Hong-Li,WANG Lu.Particle Swarm Optimization Algorithm Based on Beetle Antennae Search for Solving Portfolio Problem.COMPUTER SYSTEMS APPLICATIONS,2019,28(2):171-176

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