一种改进的量子粒子群优化算法及其应用

为提高基于概率幅编码的量子粒子群算法的优化效率,提出了一种改进的量子粒子群优化算法。在改进的算法中,采用量子Hadamard门实现粒子位置的变异,将概率幅对换变异改进为更具柔韧性的旋转调整,有效避免了种群在搜索空间中多样性的丢失;通过分析惯性因子、自身因子和全局因子的关系,提出了一种根据粒子当前适应度自适应确定全局因子的方法。以函数极值优化问题为例,仿真结果表明改进算法的搜索能力和优化效率优于原量子粒子群算法。     

基于差分进化的多目标粒子群特征选择算法

特征选择技术在大数据分析、图像处理、生物信息学等领域具有重要作用。在实际应用中,降低分类错误率和减少提取出的特征数量便于后续数据的利用,往往是两个冲突的目标。基于拥挤、变异和支配策略的多目标粒子群特征选择（crowding,mutation,dominance particle swarm optimization for feature selection,CMDPSOFS）算法是一种面向特征选择应用中特征数量最小和分类错误率最低的双目标优化算法。它使用三种不同的变异机制,用于保持群体多样性和平衡全局、局部搜索的能力,但其中的均匀变异使算法的随机性大大增加,产生较多适应值差的解,降低了算法收敛速度。改进的CMDPSOFS-II算法将差分进化算法中的变异算子和选择操作引入到CMDPSOFS算法中,实验结果表明,CMDPSOFS-II算法在特征选择上得到比原来的方法更优的结果,更好地平衡了全局和局部搜索能力。     

Improved email spam detection model with negative selection algorithm and particle swarm optimization

The adaptive nature of unsolicited email by the use of huge mailing tools prompts the need for spam detection. Implementation of different spam detection methods based on machine learning techniques was proposed to solve the problem of numerous email spam ravaging the system. Previous algorithm used in email spam detection compares each email message with spam and non-spam data before generating detectors while our proposed system inspired by the artificial immune system model with the adaptive nature of negative selection algorithm uses special features to generate detectors to cover the spam space. To cope with the trend of email spam, a novel model that improves the random generation of a detector in negative selection algorithm (NSA) with the use of stochastic distribution to model the data point using particle swarm optimization (PSO) was implemented. Local outlier factor is introduced as the fitness function to determine the local best (Pbest) of the candidate detector that gives the optimum solution. Distance measure is employed to enhance the distinctiveness between the non-spam and spam candidate detector. The detector generation process was terminated when the expected spam coverage is reached. The theoretical analysis and the experimental result show that the detection rate of NSA–PSO is higher than the standard negative selection algorithm. Accuracy for 2000 generated detectors with threshold value of 0.4 was compared. Negative selection algorithm is 68.86% and the proposed hybrid negative selection algorithm with particle swarm optimization is 91.22%.     

Gene selection using hybrid particle swarm optimization and genetic algorithm

Selecting high discriminative genes from gene expression data has become an important research. Not only can this improve the performance of cancer classification, but it can also cut down the cost of medical diagnoses when a large number of noisy, redundant genes are filtered. In this paper, a hybrid Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) method is used for gene selection, and Support Vector Machine (SVM) is adopted as the classifier. The proposed approach is tested on three benchmark gene expression datasets: Leukemia, Colon and breast cancer data. Experimental results show that the proposed method can reduce the dimensionality of the dataset, and confirm the most informative gene subset and improve classification accuracy.     

融合Shapley值和粒子群优化算法的混合特征选择算法

针对在模式分类问题中,数据往往存在不相关的或冗余的特征,从而影响分类的准确性的问题,提出一种融合Shapley值和粒子群优化算法的混合特征选择算法,以利用最少的特征获得最佳分类效果。在粒子群优化算法的局部搜索中引入博弈论的Shapley值,首先计算粒子（特征子集）中每个特征对分类效果的贡献值（Shapley值）,然后逐步删除Shapley值最低的特征以优化特征子集,进而更新粒子,同时也增强了算法的全局搜索能力,最后将改进后的粒子群优化算法运用于特征选择,以支持向量机分类器的分类性能和选择的特征数目作为特征子集评价标准,对UCI机器学习数据集和基因表达数据集的17个具有不同特征数量的医疗数据集进行分类实验。实验结果表明所提算法能有效地删除数据集中55%以上不相关的或冗余的特征,尤其对于中大型数据集能删减80%以上,并且所选择的特征子集也具有较好的分类能力,分类准确率能提高2至23个百分点。     

改进的基于粒子群优化的支持向量机特征选择和参数联合优化算法

针对支持向量机(SVM)中特征选择和参数优化对分类精度有较大影响,提出了一种改进的基于粒子群优化(PSO)的SVM特征选择和参数联合优化算法(GPSO-SVM),使算法在提高分类精度的同时选取尽可能少的特征数目。为了解决传统粒子群算法在进行优化时易出现陷入局部最优和早熟的问题,该算法在PSO中引入遗传算法(GA)中的交叉变异算子,使粒子在每次迭代更新后进行交叉变异操作来避免这一问题。该算法通过粒子之间的不相关性指数来决定粒子之间的交叉配对,由粒子适应度值的大小决定其变异概率的大小,由此产生新的粒子进入到群体中。这样使得粒子跳出当前搜索到的局部最优位置,提高了群体的多样性,在全局范围内寻找更优值。在不同数据集上进行实验,与基于PSO和GA的特征选择和SVM参数联合优化算法相比,GPSO-SVM的分类精度平均提高了2%~3%,选择的特征数目减少了3%~15%。实验结果表明,所提算法的特征选择和参数优化效果更好。     

一种弹性粒子群优化算法

当某个粒子与最优粒子很接近时,其飞行速度将趋于零,这是粒子群优化算法容易陷入局部极小的主要原因.为此,提出一种弹性粒子群优化算法.算法中,粒子速度不依赖其与最优粒子之间距离的大小,而仅依赖于其方向信息,并采用一种自适应策略弹性地修正粒子速度的幅值.将弹性粒子群优化算法应用于几种典型测试函数的优化,数值仿真结果表明,弹性粒子群优化算法能有效地找出全局最优点.     

一种基于粒子群优化的目标跟踪特征选择算法

针对复杂背景下的运动目标跟踪特征选择问题,提出了一种基于粒子群优化的目标跟踪特征选择算法。假设具有目标与背景间最好可分离性的特征为最好的跟踪特征。通过构建目标与背景的图像特征分布方差的比值函数作为衡量目标与背景间的可分离性判据。使用粒子群优化算法优化不同的特征组合实时获取最优的目标跟踪特征。为验证该算法的有效性,将选择的最优特征与一种基于核的跟踪算法相结合进行跟踪实验。实验结果表明,算法能有效提高传统基于核的跟踪算法对于复杂场景下的运动目标跟踪的鲁棒性与准确性。     

改进的粒子群算法在烧结配料中的应用

为提高计算机烧结配料的自适应性和通用性,提出了基于改进粒子群算法优化求解的方法。该方法采用柯西分布函数演化而来的调整函数,根据迭代次数惯性权重动态调整,对粒子群算法的全局和局部搜索能力进行平衡调整,使算法初期有较快的收敛速度,后期又保持较高的寻优精度,从而提高了粒子群算法的全局和局部搜索能力。仿真结果表明,所提出的改进粒子群算法收敛速度快、精度高、具有较强的全局寻优能力,能有效降低钢铁企业烧结成本,为实际工程应用提供了一个新思路。     

混沌耗散离散粒子群算法及其在故障诊断中的应用

针对大型化工过程生产系统的高维度数据及其噪声严重影响故障诊断的性能,采用基于故障特征选择和支持向量机（SVM）的故障诊断方法.为了确保在线故障诊断的实时性和准确性,提出一种新型的混沌耗散离散粒子群（CDDPSO）算法,用于故障诊断中特征变量的搜索.仿真结果表明,CDDPSO算法能有效地搜索到全局最优解,而基于故障特征选择的故障诊断方法具有良好的故障诊断性能.     

一种改进的约束优化粒子群算法*

提出一种新的约束优化粒子群算法。该算法采用非固定多段映射罚函数法处理约束条件。在进化过程中,利用混沌序列初始化种群,选取最优粒子进行局部一维搜索,增强了在最优点附近的局部搜索能力,以加快算法的收敛速度;引入维变异方法保持种群的多样性。数值实验结果表明了该算法的有效性。     

基于克隆选择和粒子群思想的动态多群体优化算法

针对粒子群算法和克隆选择原理的特点,提出了基于克隆选择和粒子群思想的动态多群体优化算法.该算法将整个群体分为若干子群体,在子群体内部应用基本的粒子群算法,以子群体作为抗体设计了克隆、变异、选择和受体编辑算子.交异算子使子群体动态变化实现子群体闻相互交换信息,具有良好的全局搜索能力.实验结果表明,该算法具有寻优能力强、搜索精度高的优点,可用于工程问题中具有各种特性的复杂函数优化.     

Unified particle swarm delivers high efficiency to particle swarm optimization

This paper suggests integrating a unification factor into particle swarm optimization (PSO) to balance the effects of cognitive and social terms. The resultant unified particle swarm (UPS) moves particles toward the center of its personal best and the global best. This improves on PSO, which moves particles far beyond the center. Widely used benchmark functions and four types of experiments demonstrate that the proposed UPS uses slightly more computational time than PSO to attain significantly higher efficiency and, usually, better solution effectiveness and consistency than PSO. Robust performance was further demonstrated by the significantly higher efficiency and better solution effectiveness and stability achieved by the UPS, as compared to the PSO and its variants. Outstandingly, convergence speeds for the proposed UPS were very good on the 13 benchmark functions examined in experiment 1, demonstrating the correct movement of UPS particles toward convergence.     

改进粒子群算法在调制模式识别中的应用

针对标准PSO算法易陷入局部最优化和LDW-PSO算法不能适应复杂、非线性优化的问题,提出了一种基于信息熵理论的改进粒子群算法(EPSO).该方法利用信息熵值确定惯性权值,使之具有自适应地调整“探索”和“开发”的能力.将新算法应用于调制模式识别中SVM分类器最优参数值的确定,仿真研究实明,该算法性能稳定.与标准PSO和LDW-PSO算法相比,EPSO算法有效增强了跳出局部最优解的能力,具有较好的工程应用性.     

融合可行基规则的粒子群优化算法及其应用

基本粒子群优化算法对于离散的优化问题处理不佳,容易陷入局部最优。针对基本粒子群优化算法处理离散型优化问题时的缺陷,提出了一种融合可行基规则的改进型粒子群优化算法,并用该算法求解车辆路径问题。实验结果表明,该算法的优化性能和求解精度均优于其他文献算法,在求解车辆路径问题中具有较高的应用价值。     

多目标微粒群优化算法及其应用研究进展*

多目标微粒群优化(MOPSO)算法是一类基于群体智能的新型全局多目标优化方法,已受到广泛关注,并在许多领域得到应用。针对近几年来MOPSO算法及其应用的进展进行了综述和评论。首先描述了MOPSO算法的基本框架;接着对MOPSO算法进行了分类和分析,并给出了MOPSO算法的一些改进策略;然后介绍了MOPSO算法的应用进展;最后,展望了MOPSO算法值得进一步研究的方向。     

Communication latency tolerant parallel algorithm for particle swarm optimization

Particle swarm optimization (PSO) algorithm is a population-based algorithm for finding the optimal solution. Because of its simplicity in implementation and fewer adjustable parameters compared to the other global optimization algorithms, PSO is gaining attention in solving complex and large scale problems. However, PSO often requires long execution time to solve those problems. This paper proposes a parallel PSO algorithm, called delayed exchange parallelization, which improves performance of PSO on distributed environment by hiding communication latency efficiently. By overlapping communication with computation, the proposed algorithm extracts parallelism inherent in PSO. The performance of our proposed parallel PSO algorithm was evaluated using several applications. The results of evaluation showed that the proposed parallel algorithm drastically improved the performance of PSO, especially in high-latency network environment.     

改进型粒子群算法及其在选址问题中的应用

为了解决基本粒子群算法不易跳出局部最优的问题,提出了一种协同粒子群优化算法。在算法中通过加入权值递减的惯性因子和变异算子以克服基本PSO易早熟、不易收敛以及缺乏多样性的不足。将算法应用于极小极大选址问题的实验结果表明,算法能够有效地求解极小极大选址问题,具有较好的应用价值。     

两群微粒群优化算法及其应用

针对微粒群优化算法容易陷入局部极值的缺陷,提出两群微粒群优化算法．通过对5种常用测试函数进行测试和比较,结果表明两群微粒群优化算法比基本微粒群优化算法更容易找到全局最优解,优化效率明显提高．然后将两群微粒群优化算法用于催化裂化装置主分馏塔轻柴油95%点软测量建模,通过与实际工业数据对比,表明该软测量模型具有高的精度、好的性能和广阔的应用前景．     

Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm

This letter presents a formal stochastic convergence analysis of the standard particle swarm optimization (PSO) algorithm, which involves with randomness. By regarding each particle's position on each evolutionary step as a stochastic vector, the standard PSO algorithm determined by non-negative real parameter tuple {ω,c₁,c₂} is analyzed using stochastic process theory. The stochastic convergent condition of the particle swarm system and corresponding parameter selection guidelines are derived.