随机装卸工问题的粒子群算法

在装卸工问题的基础上提出了随机装卸工问题及其求解策略。根据问题的特点设计了相应的粒子群优化算法,并通过数值算例就其求解精度和速度与标准遗传算法进行了对比分析。     

用于分类问题的粒子群优化遗传算法

提出一种混合粒子群遗传分类算法,根据种群中个体的相互关系,采用“家族”思想对算法进行综合调控,利用家族交叉操作进行微调,并在各家族中引入粒子群思想的交叉算子,兼顾收敛速度和多样性2项指标。根据分类问题的特点,设计相应的编码方式和适应度函数,用播种的方式生成初始种群。对国际通用检验分类效果的数据集进行分类。实验结果证明,该算法的分类效果优于其他算法。     

粒子群优化算法

系统地介绍了粒子群优化算法，归纳了其发展过程中的各种改进如惯性权重、收敛因子、跟踪并优化动态目标等模型。阐述了算法在目标函数优化、神经网络训练、模糊控制系统等基本领域的应用并给出其在工程领域的应用进展，最后，对粒子群优化算法的研究和应用进行了总结和展望，指出其在计算机辅助工艺规划领域的应用前景。     

基于改进粒子群游优化的模糊逻辑系统自学习算法

Eberhart在［1］中提出了粒子群游优化算法（ParticleSwarmOptimizationAlgorithm）。该文将改进后的粒子群游算法应用于模糊逻辑系统自学习。模糊辨识器的计算机模拟证明了改进算法的有效性。     

面向端元提取的粒子群优化遗传算法

现有的粒子群优化(PSO)算法和遗传算法(GA)无法很好地解决高光谱影像端元提取这类离散解空间内的大规模取样优化问题。针对该问题,借鉴凸面几何学理论,利用局部模式粒子群优化的原理改进遗传算法,提出一种面向高光谱影像端元提取的粒子群优化遗传算法(PSOGA)。利用模拟数据和PHI影像对PSOGA算法和GA算法进行实验对比。分析结果证明,PSOGA算法的收敛速度优于GA算法。     

粒子群与多种群元胞遗传混合优化算法

元胞遗传算法通过限定个体之间的相互作用邻域提高算法的全局收敛率,但在一定程度降低搜索效率。文中提出一种粒子群与多种群元胞遗传混合优化算法。首先将群体分割成多个相互之间没有邻域关系的元胞子种群,适度降低算法的选择压力,从而更好地保持种群的多样性。算法的变异操作被粒子群算法替代,使得局部搜索能力明显提高。元胞群体分割和粒子群变异较好地均衡全局探索和局部寻优之间的关系。分析混合算法的选择压力和多样性变化规律。实验结果表明,该算法在保证搜索效率较高的同时还显著提高元胞遗传算法的全局收敛率且稳定性得到明显改善。     

Generation of Particle Swarm Optimization algorithms: An experimental study using Grammar-Guided Genetic Programming

Particle Swarm Optimization (PSO) is largely used to solve optimization problems effectively. Nonetheless, the PSO performance depends on the fine tuning of different parameters. To make the algorithm design process more independent from human intervention, some researchers have treated this task as an optimization problem. Grammar-Guided Genetic Programming (GGGP) algorithms, in particular, have been widely studied and applied in the context of algorithm optimization. GGGP algorithms produce customized designs based on a set of production rules defined in the grammar, differently from methods that simply select designs in a pre-defined limited search space. Although GGGP algorithms have been largely used in other contexts, they have not been deeply investigated in the generation of PSO algorithms. Thus, this work applies GGGP algorithms in the context of PSO algorithm design problem. Herein, we performed an experimental study comparing different GGGP approaches for the generation of PSO algorithms. The main goal is to perform a deep investigation aiming to identify pros and cons of each approach in the current task. In the experiments, a comparison between a tree-based GGGP approach and commonly used linear GGGP approaches for the generation of PSO algorithms was performed. The results showed that the tree-based GGGP produced better algorithms than the counterparts. We also compared the algorithms generated by the tree-based technique to state-of-the-art optimization algorithms, and it achieved competitive results.     

求解矩形包络问题的量子行为粒子群优化算法

介绍了一种利用量子行为粒子群算法（QPSO）求解矩形包络的方法。矩形包络是将二维不规则形状样片用它们的最佳包络矩形来代替,是服装排料的第一步。实验结果表明量子行为粒子群算法比粒子群算法,遗传算法能更好地解决求二维不规则形状样片的矩形包络的问题。     

基于粒子群优化和遗传算法的协同聚类算法

为充分发挥粒子群优化算法和遗传算法各自的优势,提出一种新的基于粒子群和遗传算法的协同进化算法,并将其应用于聚类分析。通过构建2个相互竞争的种群,采用相对适应度度量方法,在一个纯自举的过程中产生最优竞争个体。在现实世界数据集上的仿真实验表明,该算法在收敛精度方面优于基于遗传算法的聚类方法和基本粒子群优化聚类算法。     

Diagnosis of wiring networks using Particle Swarm Optimization and Genetic Algorithms

The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for wiring network diagnosis allowing the detection, localization and characterization of faults. Two complementary steps are addressed. In the first step the direct problem is modeled using RLCG circuit parameters. Then the Finite Difference Time Domain method is used to solve the telegrapher’s equations. This model provides a simple and accurate method to simulate Time Domain Reflectometry responses. In the second step the optimization methods are combined with the wire propagation model to solve the inverse problem and to deduce physical information’s about defects from the reflectometry response. Several configurations are studied in order to demonstrate the applicability of each approach. Further, in order to validate the obtained results for both inversion techniques, they are compared with experimental measurements.     

一种混合量子粒子群算法在神经网络设计中的应用

将混合量子粒子群算法（HQPSO）应用于神经网络设计,可以在对网络拓扑结构优化的同时对连接权重进行求解。该算法引入了选择机制,使优势粒子得以保留,并在训练后期使用BP算法提高训练精度,具有较高的进化效率。通过对混沌时序信号的预测,表明HQPSO算法改进了神经网络的学习性能和泛化能力。     

用于数独求解的几何粒子群优化算法设计

针对只有唯一解的数独问题(即标准数独),利用改进的几何粒子群优化算法进行求解,将几何粒子群优化算法应用到数独中,解决数独求解过程中存在的局部最优解问题。通过实例讨论求解过程中最佳参数的选择,并得出较理想的结果。实验结果表明,该方法能够有效解决数独问题。     

云计算调度算法综述

对当前云计算的调度算法进行了综述;介绍和分析了在云计算调度算法领域里比较有代表性的两个基本的算法——遗传算法和粒子群优化算法;最后对今后的研究方向进行总结和展望。     

基于混合粒子群优化算法的旅行商问题求解

针对旅行商问题提出一种混合粒子群优化算法。为了增强算法的局部搜索能力,在粒子群优化算法中加入倒置、对换等局部搜索算法。利用遗传算法全局搜索能力强的特点对用粒子群优化算法求到的解进行优化,对全局最优路径通过消除交叉路径进行优化,以进一步提高混合算法的性能。仿真结果表明,中小规模旅行商问题能够在较少的代数内收敛到较满意解。     

基于混合粒子群算法的移动机器人路径规划

为了确定复杂环境中移动机器人最优轨迹,提出了一种混合粒子群优化算法(IPSO-GOP).首先对粒子群优化算法进行改进,在算法运行的各个阶段对惯性权重进行自适应调整来增强粒子的搜索能力,并采用混沌变量对粒子进行扰动以提高收敛速度;其次,为了提高算法寻优能力,摆脱局部极小值并增加种群的多样性,引入遗传算法继承的多重交叉和变...     

Selecting informative genes from microarray data by using hybrid methods for cancer classification

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

P2P分层流媒体数据分配的粒子群遗传算法

现有P2P分层流媒体中的数据分配算法是基于贪婪思想的确定性启发式算法,不能得到全局最优解。为此,提出一种基于备选数据块编码方式的粒子群遗传算法。定义备选数据块,建立问题的无约束整数规划模型。仿真实验表明,该算法在优化效果上能比现有算法提高5%~25%。     

A novel ensemble of classifiers for microarray data classification

Micorarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes and a few hundreds of samples. Such extreme asymmetry between the dimensionality of genes and samples presents several challenges to conventional clustering and classification methods. In this paper, a novel ensemble method is proposed. Firstly, in order to extract useful features and reduce dimensionality, different feature selection methods such as correlation analysis, Fisher-ratio is used to form different feature subsets. Then a pool of candidate base classifiers is generated to learn the subsets which are re-sampling from the different feature subsets with PSO (Particle Swarm Optimization) algorithm. At last, appropriate classifiers are selected to construct the classification committee using EDAs (Estimation of Distribution Algorithms). Experiments show that the proposed method produces the best recognition rates on four benchmark databases.     

Correlation feature selection based improved-Binary Particle Swarm Optimization for gene selection and cancer classification

DNA microarray technology has emerged as a prospective tool for diagnosis of cancer and its classification. It provides better insights of many genetic mutations occurring within a cell associated with cancer. However, thousands of gene expressions measured for each biological sample using microarray pose a great challenge. Many statistical and machine learning methods have been applied to get most relevant genes prior to cancer classification. A two phase hybrid model for cancer classification is being proposed, integrating Correlation-based Feature Selection (CFS) with improved-Binary Particle Swarm Optimization (iBPSO). This model selects a low dimensional set of prognostic genes to classify biological samples of binary and multi class cancers using Naive–Bayes classifier with stratified 10-fold cross-validation. The proposed iBPSO also controls the problem of early convergence to the local optimum of traditional BPSO. The proposed model has been evaluated on 11 benchmark microarray datasets of different cancer types. Experimental results are compared with seven other well known methods, and our model exhibited better results in terms of classification accuracy and the number of selected genes in most cases. In particular, it achieved up to 100% classification accuracy for seven out of eleven datasets with a very small sized prognostic gene subset (up to <1.5%) for all eleven datasets.     

粒子群和遗传算法优化支持向量机的破产预测

介绍了一种基于粒子群算法和遗传算法优化支持向量机预测破产的方法。这种方法融合了粒子群算法、遗传算法和支持向量机诸多优点,并行地搜寻支持向量机最优的正则化参数和核参数,由此构建优化的预测模型。采用源自UCI机器学习数据库的破产和非破产混合样本数据集,随机地读入数据和进行数据预处理,运用7重交叉校验方法客观地评价预测结果。仿真结果显示,这种方法能自动有效地构建优化的支持向量机,与其他方法比较,具有更强的推广能力和更快的学习速度,而且具有更好的破产预测准确率。