改进的PSOGM算法在动态关联规则挖掘中的应用

针对动态关联规则挖掘中支持度向量和置信度向量变化趋势的分析和预测,提出一种改进的粒子群优化的灰色模型应用在动态关联规则挖掘中。由于灰色模型在引入背景值后导致在非平稳序列中的预测精度下降,因此有必要引入参数进行修正,通过在粒子群优化算法中引入二次搜索机制,优化求解灰色模型不同时刻的背景值,从而提高粒子群算法的局部搜索能力,进而提高灰色模型的预测精度。通过在Matlab平台上进行实验仿真,数据集采用超市购物数据,结果表明该方法比原始灰色模型、遗传算法优化的灰色模型和标准的粒子群优化的灰色模型具有更高的预测精度。     

基于傅里叶变换的粒子群优化算法

针对标准粒子群算法容易陷入局部收敛的问题,提出了新的优化粒子群方法,从两个方面对其进行优化.为了改进学习因子,利用傅里叶级数的特性定义了一个傅里叶级数进行分析判断;加入随机速度,辅助粒子扩大搜索区域并避免早熟.该算法遵循启发式规则,可根据粒子搜索结果动态调整参数,具有较好的全局搜索性能和搜索精度.最后,采用4种经典测试函数进行测试并比较,选取一个单峰函数和多峰函数进行仿真,仿真结果表明了该算法的可行性.     

基于链模型和粒子群的贝叶斯网结构学习算法

为提高学习贝叶斯网络结构的效率,提出一种基于链模型和粒子群的学习算法。利用包含贝叶斯网节点间因果关系信息的规则链模型来衡量拓扑序列的优劣,提高搜索的拓扑序列的质量,为粒子位置可选择的优化算法加上动态权重系数,平衡全局搜索和局部搜索,提高算法的搜索能力。实验结果表明,与I-ACO-B算法相比,该算法不仅能获得更好的解,且收敛速度也有一定的提高。     

神经网络模糊规则抽取及其应用

从功能的观点出发,提出了一种基于粒子群优化算法的神经网络模糊规则抽取方法。该方法利用所要抽取模糊规则的表达形式,设计了规则的粒子三段表示方式,在粒子群算法优化过程中,采用两种更新方法,即离散化方法和连续化方法。该方法不依赖于具体的网络结构和训练算法,可以方便地应用于各种回归型神经网络。仿真实验表明,该方法可以抽取出保真度较高的符号规则。在实际应用中,采用模糊规则抽取算法,从丙烯腈反应器软测量模型中所得到的规则,提供了一种参数调节的指导性策略。     

基于改进混沌粒子群优化算法的聚丙烯生产调优

通过算法混合提出了一种改进混沌粒子群优化算法。将混沌搜索融入到粒子群优化算法中,建立了早熟收敛判断和处理机制,显著提高了优化算法的局部搜索效率和全局搜索性能。将改进混沌粒子群优化算法应用于聚丙烯生产调优中,首先建立了聚丙烯最优牌号切换模型,然后采用改进混沌粒子群优化算法求解该最优牌号切换模型。优化结果:表明,与常规混沌粒子群优化算法相比,改进混沌粒子群优化算法具有更佳的优化效率和全局性能。     

基于多变异粒子群优化算法的模糊关联规则挖掘

针对事务数据库中连续型数值较难划分及粒子群优化算法易陷入局部最优的问题,提出一种用多变异粒子群优化算法进行模糊关联规则提取的框架,即先对连续型数值进行模糊区间划分,再通过多变异粒子群优化算法对划分结果进行模糊关联规则挖掘。分别对模糊划分方法和多变异粒子群优化算法的相关参数及框架等进行说明。在多组实验中进行比较分析,结果表明了该方法的准确性和有效性。     

双目标无等待流水线调度的加权混合算法

针对最小化“总完工时间”和“最大完工时间”的双目标无等待流水线作业调度问题提出了一种粒子群加权混合优化算法,通过随机加权的方式将其转换成单目标问题,并应用基于升序排列的ROV（ranked-order-value）编码规则,将粒子群优化算法应用于无等待流水线作业调度问题。为了提高算法的性能,增强算法的搜索能力,提出的混合算法应用了NEH方法构造初始种群,在一个较好的初始值上进行粒子群优化,为防止种群陷入局部最优造成早熟,在粒子群每次迭代之后对全局最优解加入扰动并进行变邻域搜索。仿真实验结果表明该混合调度算法具有良好的性能。     

求解job shop调度问题的量子粒子群优化算法*

针对粒子群优化算法搜索空间有限、容易出现早熟现象的缺陷,提出将量子粒子群优化算法用于求解作业车间调度问题。求解时,将每个调度按照一定的规则编码为一个矩阵,并以此矩阵作为算法中的粒子;然后根据调度目标确定目标函数,并按照量子粒子群优化算法的进化规则在调度空间内搜索最优解。仿真实例结果证明,该算法具有良好的全局收敛性能和快捷的收敛速度,调度效果优于遗传算法和粒子群优化算法。     

层次化粒子群优化算法及其在分类规则提取中的应用

介绍层次化粒子群优化算法，采用自下而上的方式在层次结构中移动粒子．将此算法应用到分类问题，用于Iris数据集的分类规则提取，并与标准的粒子群优化（Particle Swarm Optimizer，PSO）算法相比较，结果表明提取规则的精度得到提高．     

求解job-shop调度问题的量子粒子群优化算法

针对粒子群优化算法搜索空间有限、容易出现早熟现象的缺陷,提出将量子粒子群优化算法用于求解作业车间调度问题.求解时,将每个调度按照一定的规则编码为一个矩阵,并以此矩阵作为算法中的粒子;然后根据调度目标确定目标函数,并按照量子粒子群优化算法的进化规则在调度空间内搜索最优解.仿真实例结果证明,该算法具有良好的全局收敛性能和快捷的收敛速度,调度效果优于遗传算法和粒子群优化算法.     

采用优先规则的粒子群算法求解RCPSP

优先规则是解决大规模资源受限的项目调度问题(Resource-Constrained Project Scheduling Problem,RCPSP)强有力的方法,但是单一的优先规则的往往仅在某些特定的问题上表现出良好的性能。以粒子群算法为基础,提出了基于优先规则编码的粒子群算法(Priority Rule based Particle Swarm Optimization,PRPSO),求解资源受限的项目调度问题。该方法能够通过粒子群算法搜索优先规则和调度生成方案的组合。分别对PRPSO采用串行调度方案、并行调度方案和混合调度方案时,不同任务数和资源强度的问题实例进行了分析。通过对PSPLIB进行测试,结果表明该方法与其它基于优先规则的启发式方法相比有较低的偏差率,因而有较好的性能。     

利用多群体PSO算法生成分类规则

本文通过对PSO算法模型和分类模型的分析,提出了应用多群体PSO算法实现分类规则的方法。这种方法将c（c≥2）类问题看成是c个两类问题,应用c个微粒群表示c类规则,每个微粒群应用PSO算法实现对连续变量空间的分类。最后,在五个数据集上的实验结果表明了此方法的可行性和有效性,并与C4.5算法的结果进行了比较。     

Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease

Coronary artery disease (CAD) is one of the major causes of mortality worldwide. Knowledge about risk factors that increase the probability of developing CAD can help to understand the disease better and assist in its treatment. Recently, modern computer‐aided approaches have been used for the prediction and diagnosis of diseases. Swarm intelligence algorithms like particle swarm optimization (PSO) have demonstrated great performance in solving different optimization problems. As rule discovery can be modelled as an optimization problem, it can be mapped to an optimization problem and solved by means of an evolutionary algorithm like PSO. An approach for discovering classification rules of CAD is proposed. The work is based on the real‐world CAD data set and aims at the detection of this disease by producing the accurate and effective rules. The proposed algorithm is a hybrid binary‐real PSO, which includes the combination of categorical and numerical encoding of a particle and a different approach for calculating the velocity of particles. The rules were developed from randomly generated particles, which take random values in the range of each attribute in the rule. Two different feature selection methods based on multi‐objective evolutionary search and PSO were applied on the data set, and the most relevant features were selected by the algorithms. The accuracy of two different rule sets were evaluated. The rule set with 11 features obtained more accurate results than the rule set with 13 features. Our results show that the proposed approach has the ability to produce effective rules with highest accuracy for the detection of CAD.     

基于仿生学改进的粒子群算法

针对标准粒子群算法收敛速度慢和易陷入局部最优的局限性,提出了一种基于仿生学改进的粒子群算法。即通过在标准粒子群公式中加入负梯度项,使算法更加符合鸟群觅食的实际规律,同时使算法的全局和局部搜索能力得到了平衡。仿真对比结果表明,改进的粒子群算法减小了陷入局部极值的可能性,能够提高最优解的精度和优化效率。     

Chaotic particle swarm optimization for assembly sequence planning

Assembly sequence planning of complex products is difficult to be tackled, because the size of the search space of assembly sequences is exponentially proportional to the number of parts or components of the products. Contrasted with the conventional methods, the intelligent optimization algorithms display their predominance in escaping from the vexatious trap. This paper proposes a chaotic particle swarm optimization (CPSO) approach to generate the optimal or near-optimal assembly sequences of products. Six kinds of assembly process constraints affecting the assembly cost are concerned and clarified at first. Then, the optimization model of assembly sequences is presented. The mapping rules between the optimization model and the traditional PSO model are given. The variable velocity in the traditional PSO algorithm is changed to the velocity operator (vo) which is used to rearrange the parts in the assembly sequences to generate the optimal or near-optimal assembly sequences. To improve the quality of the optimal assembly sequence and increase the convergence rate of the traditional PSO algorithm, the chaos method is proposed to provide the preferable assembly sequences of each particle in the current optimization time step. Then, the preferable assembly sequences are considered as the seeds to generate the optimal or near-optimal assembly sequences utilizing the traditional PSO algorithm. The proposed method is validated with an illustrative example and the results are compared with those obtained using the traditional PSO algorithm under the same assembly process constraints.     

Optimal sensor rules and unified fusion rules for multisensor multi-hypothesis network decision systems with channel errors

In this paper, for general jointly distributed sensor observations, we present optimal sensor rules with channel errors for a given fusion rule. Then, the unified fusion rules problem for multisensor multi-hypothesis network decision systems with channel errors is studied as an extension of our previous results for ideal channels, i.e., people only need to optimize sensor rules under the proposed unified fusion rules to achieve global optimal decision performance. More significantly, the unified fusion rules do not depend on distributions of sensor observations, decision criterion, and the characteristics of fading channels. Finally, several numerical examples support the above analytic results and show some interesting phenomena which can not be seen in ideal channel case.     

一个最优分类关联规则算法

分类和关联规则发现是数据挖掘中的两个重要领域。使用关联规则算法挖掘分类规则被叫做分类关联规则算法,是一个有较好前景的方法。本文提出了一个最优分类关联规则算法——OCARA。该算法使用最优关联规则挖掘算法挖掘分类规则,并对最优规则集排序,从而获得一个分类精度较高的分类器。将OCARA与传统分类算法C4.5和一般分类关联规则算法CBA、RMR在8个UCI数据集上进行实验比较,结果显示OCARA具有更好的性能,证明OCARA是一个有效的分类关联规则挖掘算法。     

A heuristic method for learning Bayesian networks using discrete particle swarm optimization

Bayesian networks are a powerful approach for representing and reasoning under conditions of uncertainty. Many researchers aim to find good algorithms for learning Bayesian networks from data. And the heuristic search algorithm is one of the most effective algorithms. Because the number of possible structures grows exponentially with the number of variables, learning the model structure from data by considering all possible structures exhaustively is infeasible. PSO (particle swarm optimization), a powerful optimal heuristic search algorithm, has been applied in various fields. Unfortunately, the classical PSO algorithm only operates in continuous and real-valued space, and the problem of Bayesian networks learning is in discrete space. In this paper, two modifications of updating rules for velocity and position are introduced and a Bayesian networks learning based on binary PSO is proposed. Experimental results show that it is more efficient because only fewer generations are needed to obtain optimal Bayesian networks structures. In the comparison, this method outperforms other heuristic methods such as GA (genetic algorithm) and classical binary PSO.     

Automatically extracting T–S fuzzy models using cooperative random learning particle swarm optimization

This paper proposes a methodology for automatically extracting T–S fuzzy models from data using particle swarm optimization (PSO). In the proposed method, the structures and parameters of the fuzzy models are encoded into a particle and evolve together so that the optimal structure and parameters can be achieved simultaneously. An improved version of the original PSO algorithm, the cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of PSO. CRPSO employs several sub-swarms to search the space and the useful information is exchanged among them during the iteration process. Simulation results indicate that CRPSO outperforms the standard PSO algorithm, genetic algorithm (GA) and differential evolution (DE) on the functions optimization and benchmark modeling problems. Moreover, the proposed CRPSO-based method can extract accurate T–S fuzzy model with appropriate number of rules.     

新型决策树构造方法

决策树是一种重要的数据挖掘工具,但构造最优决策树是一个NP-完全问题。提出了一种基于关联规则挖掘的决策树构造方法。首先定义了高可信度的近似精确规则,给出了挖掘这类规则的算法;在近似精确规则的基础上产生新的属性,并讨论了新生成属性的评价方法;然后利用新生成的属性和数据本身的属性共同构造决策树;实验结果表明新的决策树构造方法具有较高的精度。