结构重组的细菌觅食优化算法及其在投资组合问题上的应用

发布时间：2018-05-13 08:21

本文选题：细菌觅食 + 执行结构　；参考：《中国管理科学》2014年S1期

【摘要】：为了提高细菌觅食优化(BFO)算法的收敛速度,降低它的计算复杂性,从结构设计的新思路提出了结构重组的细菌觅食优化(SRBFC)算法。借鉴粒子群算法(PSO)中单循环结构的优点,对细菌觅食算法的结构重新设计,采用复制频数、驱散-死亡频数、趋化次数三个参数来判断复制、驱散-死亡操作的进行,在不违背原始算法的基本思想的前提下,将嵌套循环结构简化为新的执行结构。为了验证该算法的有效性以及可行性,首先将SRBFO算法与BFO算法在四个常见的测试函数上进行仿真,结果表明SRBFO算法既保持了原算法的优点,又能大大降低计算时间。接着将SRBFO算法应用于求解带有交易费用和不允许卖空的投资组合优化问题。考虑了三种对风险态度的投资者,选择了较为困难的8个资产的投资问题进行仿真,并将结果与PSO和SPSO算法对比,再一次证明了SRBFO算法在寻找最优投资组合的有效性以及可行性。
[Abstract]:In order to improve the convergence speed and reduce the computational complexity of bacterial foraging optimization (BFOO) algorithm, a new structural reconfiguration algorithm for bacterial foraging optimization (SRBFC) is proposed. Based on the advantages of single cycle structure in particle swarm optimization (PSO), this paper redesigns the structure of bacterial foraging algorithm and uses three parameters: copy frequency, dispers-death frequency and chemotaxis number to judge the operation of replication, dispersal and death. Without violating the basic idea of the original algorithm, the nested loop structure is simplified to a new execution structure. In order to verify the validity and feasibility of the algorithm, the SRBFO algorithm and the BFO algorithm are first simulated on four common test functions. The results show that the SRBFO algorithm not only retains the advantages of the original algorithm, but also greatly reduces the computational time. Then the SRBFO algorithm is applied to solve the portfolio optimization problem with transaction cost and no short selling. Considering three kinds of investors with risk attitude, the paper chooses the more difficult investment problem of eight assets to carry on the simulation, and compares the result with PSO and SPSO algorithm. The validity and feasibility of SRBFO algorithm in finding the optimal portfolio are proved again.
【作者单位】：深圳大学管理学院;香港理工大学工学院;湖北大学商学院;
【分类号】：C934;;F830.59

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