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。     

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。     

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。     

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.     

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     

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.     

非线性方程组问题的粒子群-邻近点混合算法

非线性方程组问题是一类经典的数值计算问题,单纯的进化算法不但需要很高的进化代数,而且也不能保证100％收敛到全局最优解。为求解此问题,把粒子群算法和邻近点算法相混合,利用邻近点算法作为外层算法,粒子群算法作为内层算法进行求解。实验结果表明该算法对凸问题有较好的计算效果,是求解非线性方程组问题的一种有效算法。     

Development of a fuzzy goal programming model for optimization of lead time and cost in an overlapped product development project using a Gaussian Adaptive Particle Swarm Optimization-based approach

The aim of this paper is to present a model-based methodology to estimate the optimal amount of overlapping and communication policy with a view to minimizing product development lead time and cost. In the first step of methodology, the underlying two factors are considered in order to formulate mathematically a multi-objective function for a complete product development project. To add these objectives, incommensurate in nature, a fuzzy goal programming-based approach is adopted as the second step. In order to attain the optimal solution of formulated objective function, this paper introduces a novel approach, “Gaussian Adaptive Particle Swarm Optimization” (GA-PSO), which is embedded with two beneficial attributes: (1) Gaussian probability distribution, and (2) Time-Varying Acceleration Coefficients strategy. An illustrative hypothetical example of mobile phones is detailed to demonstrate the proposed model-based methodology. Experiments are performed on an underlying example, and computational results are reported to support the efficacy of the proposed model.     

组卷的遗传算法设计

提出一种改进的遗传算法作为组卷的策略.染色体采用符号编码设计,解决了遗传运算过程中满足约束条件的问题.采用非优超排序法对染色体进行评价,在选择算子的设计上,既能够复制一部分较好的个体,又体现了选择的概率性.变异概率和交叉概率能随个体的不同适应度自适应改变,同时变异概率随种群多样性自适应变化.采用基于数据仓库的最优解保存策略,使搜索结果呈现出丰富的Pareto解集.     

用基于多目标决策的遗传算法解网络拓扑结构设计问题

多点网络拓扑结构设计问题是NP-完全问题。该文提出了一个基于多目标决策的遗传算法(MCGA)来解决多点网络拓扑结构问题。和其它多目标遗传算法不同的是:首先,对网络节点进行预划分,使得Pareto优的节点归于候选分枝节点集合;其次,修改了Prüfer编码,使得编码中的码元代表候选分枝节点,以利于对分枝节点的搜索;最后,构造了分枝变异算子与非分枝变异算子作为主要的进化算子。该算法以概率1收敛于全局最优解集。数值实验表明该算法优于其它多目标遗传算法。     

基于模糊Petri网和Genetic-PSO算法的动态不确定性知识表示方法

针对复杂系统中不确定性信息的演变特性,提出对其动态适应的基于模糊Petri网和遗传-粒子群(GPSO)算法的不确定性知识表示方法.在基于模糊Petri网的不确定性知识表示模型的基础上,对该模型进行精确数学表示,并采用GPSO实现对不确定性表征参数的动态求解和自学习.最后通过在运载火箭伺服机构故障诊断上的应用验证基于GPSO的自学习模糊Petri网的有效性.     

基于免疫遗传神经网络的CRM数据挖掘模型的设计与实现

客户分类是智能CR M系统的一个重要功能。该文提出使用前馈型神经网络构作一个CRM客户分类模型的思想,并用免疫遗传算法对其进行优化。在染色体设计上提出了三层结构的染色体,解决单层结构染色体中,短基因组实际变异机率过小的问题,促使子代种群有更好的“生物多样性”。最后通过实验证明模型有较好的客户类别识别率及优点。     

基于粒子群优化的模糊c均值聚类算法--在丙烯腈反应器参数优化上的应用

采用粒子群优化(PSO)算法,代替遗传算法(GA),将其和模糊c均值(FCM)聚类算法结合,形成基于粒子群优化的模糊c均值聚类(PSO-FCM)算法,同时引进混沌优化算法加强PSO-FCM算法的局部搜索能力。以某工厂丙烯腈反应器数据为研究对象,对比GA-FCM算法和FCM算法,研究结果表明PSO-FCM算法能够得到较优的聚类,且该算法实现简单,便于工程应用,对丙烯腈反应器参数调整的指导作用更加显著。     

基于时序数据的软件可靠性模型组合新方法

基于时序数据的软件可靠性模型受到越来越多的关注,然而单一模型在精确度和通用性上都存在不足,鉴于此,提出一种新的软件可靠性模型组合的方法,该方法将反向传播（ BP ）神将网络模型和支持向量机回归（SVMR）模型进行组合,通过遗传算法（GA）和滑动窗口机制构造可靠性模型输入,使用粒子群（PSO）算法选择单一模型的最优参数,并使用BP神经网络确定两个模型的权重值建立组合模型,来预测下一阶段的软件失效数据。最后进行了仿真实验并做了对比分析,结果表明该方法较单一模型具有更高的精确度和较好的通用性。     

Integration of PSO and GA for optimum design of fuzzy PID controllers in a pendubot system

In this paper, a novel auto-tuning method is proposed to design fuzzy PID controllers for asymptotical stabilization of a pendubot system. In the proposed method, a fuzzy PID controller is expressed in terms of fuzzy rules, in which the input variables are the error signals and their derivatives, while the output variables are the PID gains. In this manner, the PID gains are adaptive and the fuzzy PID controller has more flexibility and capability than the conventional ones with fixed gains. To tune the fuzzy PID controller simultaneously, an evolutionary learning algorithm integrating particle swarm optimization (PSO) and genetic algorithm (GA) methods is proposed. The simulation results illustrate that the proposed method is indeed more efficient in improving the asymptotical stability of the pendubot system. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Detection of doors using a genetic visual fuzzy system for mobile robots

Doors are common objects in indoor environments and their detection can be used in robotic tasks such as map-building, navigation and positioning. This work presents a new approach to door-detection in indoor environments using computer vision. Doors are found in gray-level images by detecting the borders of their architraves. A variation of the Hough Transform is used in order to extract the segments in the image after applying the Canny edge detector. Features like length, direction, or distance between segments are used by a fuzzy system to analyze whether the relationship between them reveals the existence of doors. The system has been designed to detect rectangular doors typical of many indoor environments by the use of expert knowledge. Besides, a tuning mechanism based on a genetic algorithm is proposed to improve the performance of the system according to the particularities of the environment in which it is going to be employed. A large database of images containing doors of our building, seen from different angles and distances, has been created to test the performance of the system before and after the tuning process. The system has shown the ability to detect rectangular doors under heavy perspective deformations and it is fast enough to be used for real-time applications in a mobile robot.     

Partial imputation of unseen records to improve classification using a hybrid multi-layered artificial immune system and genetic algorithm

Missing data in large insurance datasets affects the learning and classification accuracies in predictive modelling. Insurance datasets will continue to increase in size as more variables are added to aid in managing client risk and will therefore be even more vulnerable to missing data. This paper proposes a hybrid multi-layered artificial immune system and genetic algorithm for partial imputation of missing data in datasets with numerous variables. The multi-layered artificial immune system creates and stores antibodies that bind to and annihilate an antigen. The genetic algorithm optimises the learning process of a stimulated antibody. The evaluation of the imputation is performed using the RIPPER, k-nearest neighbour, naïve Bayes and logistic discriminant classifiers. The effect of the imputation on the classifiers is compared with that of the mean/mode and hot deck imputation methods. The results demonstrate that when missing data imputation is performed using the proposed hybrid method, the classification improves and the robustness to the amount of missing data is increased relative to the mean/mode method for data missing completely at random (MCAR) missing at random (MAR), and not missing at random (NMAR).The imputation performance is similar to or marginally better than that of the hot deck imputation.     

Developing monthly operating rules for a cascade system of reservoirs: Application of Bayesian Networks

In this paper, a Bayesian Network (BN) is utilized for developing monthly operating rules for a cascade system of reservoirs which is mainly aimed to control floods and supply irrigation needs. BN is trained and verified using the results of a reservoir operation optimization model, which optimizes monthly releases from cascade reservoirs. The inputs of the BN are monthly inflows, reservoir storages at the beginning of the month, and downstream water demands. The trained BN provides the probability distribution functions of reservoirs' releases for each set of input data. The long-term optimization model in monthly scale is formulated to minimize the expected flood and agricultural water deficit damages. The optimization model is developed using an extended version of the Varying chromosome Length Genetic Algorithm (VLGA-II). To incorporate reservoir preparedness for controlling the probable floods in each month, damages associated with floods with different return periods have been considered in the optimization model. For this purpose, a short-term optimization model which provides the optimal hourly releases during floods is utilized and linked to a flood damage estimation model. Damages due to deficit in supplying agricultural water demands are also calculated based on the functions of crop yield responses to deficit irrigation. The developed models are applied to the cascade system of the Dez and Bakhtiari Reservoirs in Southwest of Iran. The result of the trained BN is compared with the rules developed using classical and fuzzy linear regressions and it is shown that the total damage obtained by the BN-based operating rules is about 60 percent less than the total damage obtained using the fuzzy and classical regression analyses. The average relative error in estimating optimal releases is also reduced about 30 percent by using the BN-based rules.     

A novel neural network ensemble architecture for time series forecasting

We propose a novel homogeneous neural network ensemble approach called Generalized Regression Neural Network (GEFTS–GRNN) Ensemble for Forecasting Time Series, which is a concatenation of existing machine learning algorithms. GEFTS uses a dynamic nonlinear weighting system wherein the outputs from several base-level GRNNs are combined using a combiner GRNN to produce the final output. We compare GEFTS with the 11 most used algorithms on 30 real datasets. The proposed algorithm appears to be more powerful than existing ones. Unlike conventional algorithms, GEFTS is effective in forecasting time series with seasonal patterns.