混沌时间序列的混合粒子群优化预测

提出一种混合粒子群优化算法,即在改进粒子群优化算法全局搜索模型参数的基础上,利用梯度下降法进一步确定径向基神经网络模型参数,以提高网络的收敛精度和网络性能.采用基于RBFNN的混合粒子群优化算法进行离散Henon和连续Mackey-Glass混沌时间序列预测仿真,结果表明该算法能快速精确地预测混沌时间序列,是研究复杂非线性动力系统辨识和控制的一种有效方法.     

Design of K-means clustering-based polynomial radial basis function neural networks (pRBF NNs) realized with the aid of particle swarm optimization and differential evolution

In this paper, we introduce an advanced architecture of K-means clustering-based polynomial Radial Basis Function Neural Networks (p-RBF NNs) designed with the aid of Particle Swarm Optimization (PSO) and Differential Evolution (DE) and develop a comprehensive design methodology supporting their construction. The architecture of the p-RBF NNs comes as a result of a synergistic usage of the evolutionary optimization-driven hybrid tools. The connections (weights) of the proposed p-RBF NNs being of a certain functional character and are realized by considering four types of polynomials. In order to design the optimized p-RBF NNs, a prototype (center value) of each receptive field is determined by running the K-means clustering algorithm and then a prototype and a spread of the corresponding receptive field are further optimized through running Particle Swarm Optimization (PSO) and Differential Evolution (DE). The Weighted Least Square Estimation (WLSE) is used to estimate the coefficients of the polynomials (which serve as functional connections of the network). The performance of the proposed model and the comparative analysis involving models designed with the aid of PSO and DE are presented in case of a nonlinear function and two Machine Learning (ML) datasets     

Cooperative learning for radial basis function networks using particle swarm optimization

This paper presents a new evolutionary cooperative learning scheme, able to solve function approximation and classification problems with improved accuracy and generalization capabilities. The proposed method optimizes the construction of radial basis function (RBF) networks, based on a cooperative particle swarm optimization (CPSO) framework. It allows for using variable-width basis functions, which increase the flexibility of the produced models, while performing full network optimization by concurrently determining the rest of the RBF parameters, namely center locations, synaptic weights and network size. To avoid the excessive number of design variables, which hinders the optimization task, a compact representation scheme is introduced, using two distinct swarms. The first swarm applies the non-symmetric fuzzy means algorithm to calculate the network structure and RBF kernel center coordinates, while the second encodes the basis function widths by introducing a modified neighbor coverage heuristic. The two swarms work together in a cooperative way, by exchanging information towards discovering improved RBF network configurations, whereas a suitably tailored reset operation is incorporated to help avoid stagnation. The superiority of the proposed scheme is illustrated through implementation in a wide range of benchmark problems, and comparison with alternative approaches.     

混合粒子群优化算法优化前向神经网络结构和参数

提出了综合利用粒子群优化算法（PSO）和离散粒子群优化算法（D-PSO）同时优化前向神经网络结构和参数的新方法。该算法使用离散粒子群优化算法优化神经网络连接结构,用多维空间中0或1取值的粒子来描述所有可能的神经网络连接,同时使用粒子群优化算法优化神经网络权值。将经过该算法训练的神经网络应用于故障诊断,能够有效消除冗余连接结构对网络诊断能力的影响。仿真试验的结果表明,相比遗传算法等其他算法,该算法能够有效改善神经网络结构和参数的优化效率,提高故障模式识别的准确率。     

The improved radial basis function neural network and its application

Compared with other feed-forward neural networks, radial basis function neural networks (RBFNN) have many advantages which make them more suitable for nonlinear system modeling, and they have recently received considerable attention. In this paper, a RBFNN is employed to model strongly nonlinear systems. First, the problems of nonlinear system modeling are analyzed, and then the structure of the RBFNN as well as the training algorithm are improved to solve these problems. Finally, an industrial high-purity distillation column, which is a strongly nonlinear system, is successfully modeled with the improved RBFNN. Owing to the complexities of a nonlinear system, it is necessary to use a real-time model correction method to modify the parameters of the RBFNN model in real time. One efficient method is proposed in this paper. The idea is to employ the Givens transformation to modify the parameters of the RBFNN-based model. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

混沌混合粒子群算法应用于优化酯化反应神经网络模型

利用混沌映射的随机性和遍历性,将其引入粒子群优化算法,以提高算法的全局寻优能力,同时引入优进策略,以改善其局部寻优效率,在此基础上构建了混沌混合粒子群优化算法(CHPSO)。高维复杂函数的仿真优化试验表明,CHPSO全局寻优能力强、优化效率高。针对常规算法训练神经网络容易早熟收敛和陷入局部极值点的不足,采用CHPSO训练人工神经网络,由此构建CHPSO-NN模型,并应用于乙酸己酯催化酯化反应条件的建模,与BP-NN相比,其预测能力和稳健性都有较大提高,效果良好,与传统方法相比有明显的优越性。     

A new hybrid particle swarm and simulated annealing stochastic optimization method

A novel hybrid particle swarm and simulated annealing stochastic optimization method is proposed. The proposed hybrid method uses both PSO and SA in sequence and integrates the merits of good exploration capability of PSO and good local search properties of SA. Numerical simulation has been performed for selection of near optimum parameters of the method. The performance of this hybrid optimization technique was evaluated by comparing optimization results of thirty benchmark functions of different dimensions with those obtained by other numerical methods considering three criteria. These criteria were stability, average trial function evaluations for successful runs and the total average trial function evaluations considering both successful and failed runs. Design of laminated composite materials with required effective stiffness properties and minimum weight design of a three-bar truss are addressed as typical applications of the proposed algorithm in various types of optimization problems. In general, the proposed hybrid PSO-SA algorithm demonstrates improved performance in solution of these problems compared to other evolutionary methods The results of this research show that the proposed algorithm can reliably and effectively be used for various optimization problems.     

一种自适应混合粒子群优化算法及其应用*

为提高粒子群算法的寻优精度,提出一种将单纯形法(SM)和粒子群(PSO)算法相结合的自适应混合粒子群优化(AHPSO)算法,该算法根据进化需要动态调整粒子的惯性权重,并在进化停滞时使用SM优化。通过仿真实验证明了AHPSO的寻优性能优于SPSO和SMPSO。将AHPSO用于某航空发动机的PID参数优化,其整定性能优于现有的工业方法和其他PSO算法。     

基于PSO和BP复合算法的模糊神经网络控制器

为了克服单独应用粒子群算法（PSO）或BP算法训练模糊神经网络控制器参数时存在的缺陷,提出了一种训练模糊神经网络参数的PSO＋BP算法。该算法将二者相结合,即在PSO算法中加入一个BP算子,以充分利用PSO算法的全局寻优能力和BP算法的局部搜索能力,从而更有效地提高其收敛速度、训练效率和提高该模糊神经网络控制器的控制效果。最后的仿真实验结果验证了该基于PSO＋BP复合算法的模糊神经网络控制器的有效性和可行性。     

Meta-modeling using generalized regression neural network and particle swarm optimization

Swarm intelligence (SI) and evolutionary computation (EC) algorithms are often used to solve various optimization problems. SI and EC algorithms generally require a large number of fitness function evaluations (i.e., higher computational requirements) to obtain quality solutions. This requirement becomes more challenging when optimization problems are associated with computationally expensive analyses and/or simulation tasks. To tackle this issue, meta-modeling has shown successful results in improving computational efficiency by approximating the fitness or constraint functions of these complex optimization problems. Meta-modeling approaches typically use polynomial regression, kriging, radial basis function network, and support vector machines. Less attention has been given to the generalized regression neural network approach, and yet, it offers several advantages. Specifically, the model construction process does not require iterations. Its only one parameter is known to be less sensitive and usually requires less effort in selecting an optimal parameter. We use generalized regression neural network in this paper to construct meta-models and to approximate the fitness function in particle swarm optimization. To assess the performance and quality of these solutions, the proposed meta-modeling approach is tested on ten benchmark functions. The results are promising in terms of the solution quality and computational efficiency, especially when compared against the results of particle swarm optimization without meta-modeling and several other meta-modeling methods in previously published literature.     

Improving the effect of fuzzy clustering on RBF network’s performance in terms of particle swarm optimization

This paper proposes a novel training algorithm for radial basis function neural networks based on fuzzy clustering and particle swarm optimization. So far, fuzzy clustering has proven to be a very efficient tool in designing such kind of networks. The motivation of the current work is to quantify the exact effect of fuzzy cluster analysis on the network’s performance and use it in order to substantially improve this performance. There are two key theoretical findings resulting from the present work. First, it is analytically proved that when the standard fuzzy c-means algorithm is used to generate the input space fuzzy partition, the main effect this partition imposes to the network’s square error (i.e. performance index) can be written down in terms of a distortion function that measures the ability of the partition to recreate the original data. Second, using the aforementioned distortion function, an upper bound of the network’s square error can be constructed. Then, the particle swarm optimization (PSO) is put in place to minimize the above upper bound and determine the network’s parameters. To further improve the accuracy, the basis function widths and the connection weights are fine-tuned by employing a steepest descent approach. The main experimental findings are: (a) the implementation of the PSO obtains a significant reduction of the square error while exhibiting a smooth dynamic behavior, (b) although the steepest descent further decreases the error it finally obtains smaller reduction rates, meaning that the strongest impact on the error reduction is provided by the PSO, and (c) the improved performance of the proposed network is demonstrated through an extensive comparison with other related methods using a 10-fold cross-validation analysis.     

Synthesis of interdigital capacitors based on particle swarm optimization and artificial neural networks

This article reports on the use of the particle swarm optimization (PSO) algorithm in the synthesis of the planar interdigital capacitor (IDC). The PSO algorithm is used to optimize the geometry parameters of the IDC in order to obtain a certain capacitance value. The capacitance value of the IDC is evaluated using an artificial neural network (ANN) model with the geometry parameters of the IDC as its inputs. Several design examples are presented that illustrate the use of the PSO algorithm, and the design goal in each example is easily achieved. Full‐wave electromagnetic simulations are also performed for some of the studied IDC structures implemented using coplanar waveguide (CPW) technology. The simulation results are in good agreement with those obtained using the ANN/PSO algorithm. © 2006 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2006.     

A hybrid algorithm based on particle swarm and chemical reaction optimization

In this paper, a hybrid method for optimization is proposed, which combines the two local search operators in chemical reaction optimization with global search ability of for global optimum. This hybrid technique incorporates concepts from chemical reaction optimization and particle swarm optimization, it creates new molecules (particles) either operations as found in chemical reaction optimization or mechanisms of particle swarm optimization. Moreover, some technical bound constraint handling has combined when the particle update in particle swarm optimization. The effects of model parameters like InterRate, γ, Inertia weight and others parameters on performance are investigated in this paper. The experimental results tested on a set of twenty-three benchmark functions show that a hybrid algorithm based on particle swarm and chemical reaction optimization can outperform chemical reaction optimization algorithm in most of the experiments. Experimental results also indicate average improvement and deviate over chemical reaction optimization in the most of experiments.     

A novel Hough transform based on eliminating particle swarm optimization and its applications

Hough transform (HT) is a well established method for curve detection and recognition due to its robustness and parallel processing capability. However, HT is quite time-consuming. In this paper, an eliminating particle swarm optimization (EPSO) algorithm is employed to improve the speed of a HT. The parameters of the solution after Hough transformation are considered as the particle positions, and the EPSO algorithm searches the optimum solution by eliminating the “weakest” particles to speed up the computation. An accumulation array in Hough transformation is utilized as a fitness function of the EPSO algorithm. The experiments on numerous images show that the proposed approach can detect curves or contours of both noise-free and noisy images with much better performance. Especially, for noisy images, it can archive much better results than that obtained by using the existing HT algorithms.     

基于粒子群算法的混合无线传感网覆盖优化

为优化混合传感网络覆盖性能,基于粒子群算法提出一种优化策略,并通过引进扰动因子,有效地避免了算法陷入早熟陷阱,加速了算法收敛。通过仿真实验,验证了该优化算法能够有效地提高网络覆盖性能,并与最新的算法进行了比较。     

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．     

一种新的RBF网络两级学习设计方法

为了简化径向基网络结构,构造出良好泛化性能力的网络,提出了一种径向基(RBF)网络的两级学习新设计方法.在下级将正交最小二乘法(OLS)与A-最优设计方法(A-opt)相结合(OLS+A-opt),引入一种基于A-最优设计准则的混合代价函数,同时优化网络模型的逼近性能及模型的充分性,自动构建结构节俭的RBF网络模型;而方法中的关键学习参数A-最优代价系数通过上级粒子群优化方法(PSO)优化获取最佳值.仿真结果表明该方法所设计的RBF网络不仅具有较好的泛化性能,而且也具有良好的模型鲁棒性及充分性,是一种有效的RBF网络设计方法.     

Improved particle swarm optimization algorithm and its application in text feature selection

Text feature selection is an importance step in text classification and directly affects the classification performance. Classic feature selection methods mainly include document frequency (DF), information gain (IG), mutual information (MI), chi-square test (CHI). Theoretically, these methods are difficult to get improvement due to the deficiency of their mathematical models. In order to further improve effect of feature selection, many researches try to add intelligent optimization algorithms into feature selection method, such as improved ant colony algorithm and genetic algorithms, etc. Compared to the ant colony algorithm and genetic algorithms, particle swarm optimization algorithm (PSO) is simpler to implement and can find the optimal point quickly. Thus, this paper attempt to improve the effect of text feature selection through PSO. By analyzing current achievements of improved PSO and characteristic of classic feature selection methods, we have done many explorations in this paper. Above all, we selected the common PSO model, the two improved PSO models based respectively on functional inertia weight and constant constriction factor to optimize feature selection methods. Afterwards, according to constant constriction factor, we constructed a new functional constriction factor and added it into traditional PSO model. Finally, we proposed two improved PSO models based on both functional constriction factor and functional inertia weight, they are respectively the synchronously improved PSO model and the asynchronously improved PSO model. In our experiments, CHI was selected as the basic feature selection method. We improved CHI through using the six PSO models mentioned above. The experiment results and significance tests show that the asynchronously improved PSO model is the best one among all models both in the effect of text classification and in the stability of different dimensions.     

一种基于混合粒子群优化算法的深度卷积神经网络架构搜索方法

神经架构搜索（neural architecture search,NAS）技术自动寻找神经网络中各层的最佳组合和连接方式,以及各种超参数的最佳分布。该方法从搜索空间生成若干不同的卷积神经网络（CNN）,使用混合粒子群优化（hybrid particle swarm optimization,HPSO）算法,将一定数目的神经网络个体视做一个群体,将每个网络个体在评价指标下的表现值视做适应度,在给定的世代数范围内,每个神经网络个体都学习自身的历史最佳适应度个体,和整个群体的最佳适应度个体,迭代改善自身的网络架构。实验结果表明,算法运行中出现的最优网络架构,在图像分类任务的多个基准数据集上,与手工设计的神经网络和以遗传算法为基础的NAS算法相比,在网络参数数量和准确率的平衡上取得了有竞争力的结果。     

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.