基于引力搜索和粒子群混合优化算法的证券投资组合问题研究

本文研究考虑交易成本的投资组合模型,分别以风险价值(VAR)和夏普比率(SR)作为投资组合的风险评价指标和效益评价指标。为有效求解此模型,本文在引力搜索和粒子群算法的基础上提出了一种混合优化算法(IN-GSA-PSO),将粒子群算法的群体最佳位置和个体最佳位置与引力搜索算法的加速度算子有机结合,使混合优化算法充分发挥单一算法的开采能力和探索能力。通过对算法相关参数的合理设置,算法能够达到全局搜索和局部搜索的平衡,快速收敛到模型的最优解。本文选取上证50股2014年下半年126个交易日的数据,运用Matlab软件进行仿真实验,实验结果显示,考虑交易成本的投资组合模型可使投资者得到更高的收益率。研究同时表明,基于PSO和GSA的混合算法在求解投资组合模型时比单一算法具有更好的性能,能够得到满意的优化结果。     

改进的粒子群算法及在CVaR模型中的应用

为了求解带有条件风险价值(CVaR)约束的均值-方差模型,提出一种基于广义学习和柯西变异的粒子群算法(CCPSO).在CCPSO算法中,为了提升种群跳出局部最优解的能力,引入一种广义学习策略,提升粒子向最优解飞行的概率;并引入一种动态变异概率,对粒子自身最优位置进行柯西变异,更好地引导种群的飞行;最后,根据全局最优粒子的运行状况,每间隔若干代对其进行变异,以产生全局新的领导者.在基准函数测试中,结果显示CCPSO算法有较好的运行结果.在CVaR模型投资组合优化中,与其它算法相比,CCPSO算法所获结果是有效的,并且优于其它算法.     

基于自适应均匀变异人工鱼群投资组合优化算法的研究

投资市场具有一定的风险,影响因素包括经济、政治、市场自身规律等,根据市场机制构建合适的投资组合模型,可以有效降低市场风险,提高投资回报率.人工鱼群算法是模仿自然界鱼类的一种人工智能优化算法,具有较好的优化能力,但有时会陷入局部最优解.首先将人工鱼群算法与均匀变异相结合,加入均匀变异随机数,使算法能够跳出局部最优解,得到全局最优,从而提高算法精度.然后采用改进人工鱼群算法对投资组合模型进行优化求解.实验表明,改进人工鱼群算法具有较好的收敛精度和收敛速度,对投资组合模型的求解效果更好,风险下降,收益增加、     

基于改进粒子群算法的复杂现实约束投资组合研究

基于可能性理论,假设各资产的未来收益率均为梯形模糊数,本文构建了带有V-型交易费用、投资比例上下限和基数约束限制的均值-方差-Yager熵模型。本文采用了带有宽容量的逐步宽容法使构建的三目标模型转化为单目标模型,通过调整宽容量的大小来控制收益和风险的大小,从而使得投资者根据自己的偏好选择适合自己的投资决策。此外,本文通过非线性惯性权重来刻画搜索速度,通过对个体最优适应度值较差的部分粒子进行初始化处理,提出了改进的粒子群算法,从而降低了陷入局部最优的可能性;同时通过0-1矩阵和放缩因子处理了基数约束和上下限约束,使得模型的求解更加有效。最后,通过实例说明了算法的可行性和有效性,给出了投资模型的有效前沿,分析了收益/风险宽容量不变时,风险/收益宽容量变化的作用,从而给投资者提供了更多的决策方案。     

基于CUDA的边界变异量子粒子群优化算法

针对量子粒子群优化算法面对复杂优化问题时,临近最优解的搜索阶段存在收敛速度慢、在边界附近全局搜索性差的问题,提出了基于CUDA的边界变异量子粒子群优化算法.GPU(图形处理器)以多颗密集的计算核心模拟粒子的搜索过程,利用并发的优势提升粒子搜索速度;边界变异则通过以随机概率将边界粒子扩散到更大的搜索域,增加种群的多样性,提升粒子群的全局搜索性.对若干优化算法的仿真实验表明,所提出方法具有较好的全局收敛性,且同等目标精度下,取得了较高的有效加速比.     

约束粒子群算法求解自融资投资组合模型研究

在马克维茨投资组合的均值-方差模型框架下,给出限制投资数量的自融资投资组合优化模型.在金融市场上有广泛应用,为了有效地求解此类问题的最优解,采用一种基于广义学习策略的约束粒子群算法(CPSO).CPSO算法具有广义的学习策略,极大地提升了种群的多样性,进而提升种群跳出局部最优解的能力.在基准函数测试中,结果显示CPSO算法有较好的运行结果.在自融资投资组合优化模型上,优化结果表明CPSO算法是可行的,有效的,并有较好的优化结果.     

基于混合单纯形算法的模糊均值图像分割

针对模糊C均值算法用于图像分割时对初始值敏感、容易陷入局部极值的问题,提出基于混合单纯形算法的模糊均值图像分割算法.算法利用Nelder-Mead单纯形算法计算量小、搜索速度快和粒子群算法自适应能力强、具有较好的全局搜索能力的特点,将混合单纯形算法的结果作为模糊C均值算法的输入,并将其用于图像分割.实验结果表明:基于混合单纯形算法的模糊均值图像分割算法在改善图像分割质量的同时,提高了算法的运行速度.     

基于量子粒子群算法的电力系统负荷多目标优化分配

针对电力系统经济负荷优化分配问题,提出了一种基于量子粒子群的多目标优化算法.该算法通过将改进后的量子进化算法融合到粒子群中,采用量子位对粒子的当前位置进行编码,用量子旋转门实现对粒子最优位置的搜索,用量子非门实现粒子位置的变异以避免早熟收敛.这种搜索机制能够遍历解空间,增强种群的多样性,并能用量子位的概率幅将最优解表述为解空间中的多种表述形式,从而增强全局最优的可能性.最后,通过算例进行仿真分析,结果表明算法的搜索能力和优化效率均优于普通粒子群算法.     

基于IAFSA-MUSIC算法的阵列信号DOA估计

针对传统MUSIC算法运算量过大以及低信噪比下分辨率差的问题,提出将改进人工鱼群算法与MUSIC的谱峰搜索相结合,利用鱼群觅食和追逐来对解空间进行高效搜索,从而保证算法收敛的快速性和全局性.聚群的存在促使少量陷于局部最优解的人工鱼向着全局最优解的方向靠拢,提高了鱼群对不利环境的自适应性,也增强了算法的稳定性.与此同时,改进人工鱼群算法在一定程度上加快了后期收敛速度,提高了算法的估计性能.实验结果表明在低信噪比时方法相较于MUSIC而言具有更好的估计性能,并且大大减少了运算量,保证了算法的实时性.     

不同风险度量方法的投资组合比较研究

为了验证投资组合理论在中国证券市场的有效性,在不允许卖空情况,针对不同风险度量方法,文章运用旋转算法或结合序列二次规划法分别求解均值-方差、均值-下半方差投资组合模型、均值-半绝对偏差、均值-平均绝对偏差和均值-VaR.文章选取三年沪市六只业绩比较好的股票,依据前两年的数据作为样本数据,分别求出五个模型在不同期望收益率下的最优投资策略,将得出的最优投资策略应用到最后一年,进行模拟投资,从而计算出各模型的总收益率.以等比例投资为标准,比较五个模型的绩效.最后,证明了两个模型对于中国证券市场是适用.     

Particle swarm optimization: Hybridization perspectives and experimental illustrations

Metaheuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. In the present study an attempt is made to review the hybrid optimization techniques in which one main algorithm is a well known metaheuristic; particle swarm optimization or PSO. Hybridization is a method of combining two (or more) techniques in a judicious manner such that the resulting algorithm contains the positive features of both (or all) the algorithms. Depending on the algorithm/s used we made three classifications as (i) Hybridization of PSO and genetic algorithms (ii) Hybridization of PSO with differential evolution and (iii) Hybridization of PSO with other techniques. Where, other techniques include various local and global search methods. Besides giving the review we also show a comparison of three hybrid PSO algorithms; hybrid differential evolution particle swarm optimization (DE-PSO), adaptive mutation particle swarm optimization (AMPSO) and hybrid genetic algorithm particle swarm optimization (GA-PSO) on a test suite of nine conventional benchmark problems.     

一个解非线性0-1整数规划问题基于罚函数的混合粒子群优化算法

利用罚函数思想把非线性0-1整数规划问题转化为无约束最优化问题,然后把粒子群优化和罚函数方法结合构造出一个基于罚函数的混合粒子群优化算法,数值结果表明所提出的算法是有效的.     

A hybrid particle swarm optimization and bacterial foraging for power system stability enhancement

A novel hybrid approach involving particle swarm optimization (PSO) and bacterial foraging optimization algorithm (BFOA) called bacterial swarm optimization (BSO) is illustrated for designing static var compensator (SVC) in a multimachine power system. In BSO, the search directions of tumble behavior for each bacterium are oriented by the individual's best location and the global best location of PSO. The proposed hybrid algorithm has been extensively compared with the original BFOA algorithm and the PSO algorithm. Simulation results have shown the validity of the proposed BSO in tuning SVC compared with BFOA and PSO. Moreover, the results are presented to demonstrate the effectiveness of the proposed controller to improve the power system stability over a wide range of loading conditions. © 2014 Wiley Periodicals, Inc. Complexity 21: 245–255, 2015     

基于粒子群算法的非线性二层规划问题的求解算法

粒子群算法（Particle Swarm Optimization，PSO）是一种新兴的优化技术，其思想来源于人工生命和演化计算理论。PSO通过粒子追随自己找到的最好解和整个群的最好解来完成优化。该算法简单易实现，可调参数少，已得到了广泛研究和应用。本文根据该算法能够有效的求出非凸数学规划全局最优解的特点，对非线性二层规划的上下层问题求解，并根据二层规划的特点，给出了求解非线性二层规划问题全局最优解的有效算法。数值计算结果表明该算法有效。     

A hybrid glowworm swarm optimization algorithm to solve constrained multimodal functions optimization

In this paper, a novel hybrid glowworm swarm optimization (HGSO) algorithm is proposed. The HGSO algorithm embeds predatory behaviour of artificial fish swarm algorithm (AFSA) into glowworm swarm optimization (GSO) algorithm and combines the GSO with differential evolution on the basis of a two-population co-evolution mechanism. In addition, to overcome the premature convergence, the local search strategy based on simulated annealing is applied to make the search of GSO approach the true optimum solution gradually. Finally, several benchmark functions show that HGSO has faster convergence efficiency and higher computational precision, and is more effective for solving constrained multi-modal function optimization problems.     

A hybrid simplex search and particle swarm optimization for unconstrained optimization

This paper proposes the hybrid NM-PSO algorithm based on the Nelder–Mead (NM) simplex search method and particle swarm optimization (PSO) for unconstrained optimization. NM-PSO is very easy to implement in practice since it does not require gradient computation. The modification of both the Nelder–Mead simplex search method and particle swarm optimization intends to produce faster and more accurate convergence. The main purpose of the paper is to demonstrate how the standard particle swarm optimizers can be improved by incorporating a hybridization strategy. In a suite of 20 test function problems taken from the literature, computational results via a comprehensive experimental study, preceded by the investigation of parameter selection, show that the hybrid NM-PSO approach outperforms other three relevant search techniques (i.e., the original NM simplex search method, the original PSO and the guaranteed convergence particle swarm optimization (GCPSO)) in terms of solution quality and convergence rate. In a later part of the comparative experiment, the NM-PSO algorithm is compared to various most up-to-date cooperative PSO (CPSO) procedures appearing in the literature. The comparison report still largely favors the NM-PSO algorithm in the performance of accuracy, robustness and function evaluation. As evidenced by the overall assessment based on two kinds of computational experience, the new algorithm has demonstrated to be extremely effective and efficient at locating best-practice optimal solutions for unconstrained optimization.     

求解旅行商问题的基于类Kruskal的混合粒子群算法

本文针对求解旅行商问题的标准粒子群算法所存在的早熟和低效的问题,提出一种基于Greedy Heuristic的初始解与粒子群相结合的混合粒子群算法(SKHPSO)。该算法通过本文给出的类Kruskal算法作为Greedy Heuristic的具体实现手段,产生一个较优的初始可行解,作为粒子群中的一员,然后再用改进的混合粒子群算法进行启发式搜索。SKHPSO的局部搜索借鉴了Lin-Kernighan邻域搜索,而全局搜索结合了遗传算法中的交叉及置换操作。应用该算法对TSPLIB中的典型算例进行了算法测试分析,结果表明:SKHPSO可明显提高求解的质量和效率。     

非线性约束优化问题的混合粒子群算法

把处理约束条件的一个外点方法和改进的粒子群优化算法相结合,提出了一种求解非线性约束优化问题的混合粒子群优化算法.该方法兼顾了粒子群优化和外点法的优点,对算法迭代过程中出现不可行粒子,利用外点法处理后产生可行粒子.数值实验表明了提出的新算法具有有效性、通用性和稳健性.     

A Particle Swarm Optimization Algorithm with Path Relinking for the Location Routing Problem

This paper introduces a new hybrid algorithmic nature inspired approach based on particle swarm optimization, for solving successfully one of the most popular logistics management problems, the location routing problem (LRP). The proposed algorithm for the solution of the location routing problem, the hybrid particle swarm optimization (HybPSO-LRP), combines a particle swarm optimization (PSO) algorithm, the multiple phase neighborhood search – greedy randomized adaptive search procedure (MPNS-GRASP) algorithm, the expanding neighborhood search (ENS) strategy and a path relinking (PR) strategy. The algorithm is tested on a set of benchmark instances. The results of the algorithm are very satisfactory for these instances and for six of them a new best solution has been found.      

Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems

Heuristic optimization provides a robust and efficient approach for solving complex real-world problems. The aim of this paper is to introduce a hybrid approach combining two heuristic optimization techniques, particle swarm optimization (PSO) and genetic algorithms (GA). Our approach integrates the merits of both GA and PSO and it has two characteristic features. Firstly, the algorithm is initialized by a set of random particles which travel through the search space. During this travel an evolution of these particles is performed by integrating PSO and GA. Secondly, to restrict velocity of the particles and control it, we introduce a modified constriction factor. Finally, the results of various experimental studies using a suite of multimodal test functions taken from the literature have demonstrated the superiority of the proposed approach to finding the global optimal solution.