Prediction approach of software fault-proneness based on hybrid artificial neural network and quantum particle swarm optimization

The identification of a module's fault-proneness is very important for minimizing cost and improving the effectiveness of the software development process. How to obtain the correlation between software metrics and module's fault-proneness has been the focus of much research. This paper presents the application of hybrid artificial neural network (ANN) and Quantum Particle Swarm Optimization (QPSO) in software fault-proneness prediction. ANN is used for classifying software modules into fault-proneness or non fault-proneness categories, and QPSO is applied for reducing dimensionality. The experiment results show that the proposed prediction approach can establish the correlation between software metrics and modules’ fault-proneness, and is very simple because its implementation requires neither extra cost nor expert's knowledge. Proposed prediction approach can provide the potential software modules with fault-proneness to software developers, so developers only need to focus on these software modules, which may minimize effort and cost of software maintenance.     

A hybrid forecasting model for enrollments based on aggregated fuzzy time series and particle swarm optimization

In this paper, a new forecasting model based on two computational methods, fuzzy time series and particle swarm optimization, is presented for academic enrollments. Most of fuzzy time series forecasting methods are based on modeling the global nature of the series behavior in the past data. To improve forecasting accuracy of fuzzy time series, the global information of fuzzy logical relationships is aggregated with the local information of latest fuzzy fluctuation to find the forecasting value in fuzzy time series. After that, a new forecasting model based on fuzzy time series and particle swarm optimization is developed to adjust the lengths of intervals in the universe of discourse. From the empirical study of forecasting enrollments of students of the University of Alabama, the experimental results show that the proposed model gets lower forecasting errors than those of other existing models including both training and testing phases.     

A novel hybrid adaptive collaborative approach based on particle swarm optimization and local search for dynamic optimization problems

This paper proposes a novel hybrid approach based on particle swarm optimization and local search, named PSOLS, for dynamic optimization problems. In the proposed approach, a swarm of particles with fuzzy social-only model is frequently applied to estimate the location of the peaks in the problem landscape. Upon convergence of the swarm to previously undetected positions in the search space, a local search agent (LSA) is created to exploit the respective region. Moreover, a density control mechanism is introduced to prevent too many LSAs crowding in the search space. Three adaptations to the basic approach are then proposed to manage the function evaluations in the way that are mostly allocated to the most promising areas of the search space. The first adapted algorithm, called HPSOLS, is aimed at improving PSOLS by stopping the local search in LSAs that are not contributing much to the search process. The second adapted, algorithm called CPSOLS, is a competitive algorithm which allocates extra function evaluations to the best performing LSA. The third adapted algorithm, called CHPSOLS, combines the fundamental ideas of HPSOLS and CPSOLS in a single algorithm. An extensive set of experiments is conducted on a variety of dynamic environments, generated by the moving peaks benchmark, to evaluate the performance of the proposed approach. Results are also compared with those of other state-of-the-art algorithms from the literature. The experimental results indicate the superiority of the proposed approach.     

A hybrid particle swarm approach based on Tribes and tabu search for multi-objective optimization

Tuning the parameters of any evolutionary algorithm is considered as a very difficult task. In this paper, we present a new adaptive multi-objective technique which consists of a hybridization between a particular particle swarm optimization approach (Tribes) and tabu search (TS) technique. The main idea behind this hybridization is to combine the rapid convergence of Tribes with the high efficient exploitation of a local search technique based on TS. In addition, we propose three different places where the local search can be applied: TS applied on the particles of the archive, TS applied only on the best particle of each tribe and TS applied on each particle of the swarm. The aim of those propositions is to study the impact of the place where the local search is applied on the performance of our hybridized Tribes. The mechanisms proposed are validated using 10 different functions from specialized literature of multi-objective optimization. The obtained results show that using this kind of hybridization is justified as it is able to improve the quality of the solutions in the majority of cases.     

改进的简化粒子群算法优化模糊神经网络建模

为了更准确地描述有记忆效应的射频功放特性,提出了一种改进的简化粒子群优化(PSO)算法,并结合自适应模糊推理系统(ANFIS)建立模糊神经网络功放模型.改进的简化PSO算法仅保留粒子的位置项,加入了随机的个体最优候选解,由粒子的当前位置、个体最优解、全局最优解和随机的个体最优候选解共同决定其位置项;采用线性递减惯性权重,并利用异步变化的动态学习因子,且新颖地引入拉普拉斯系数,从而增加了种群多样性,加快了收敛速度,避免陷入局部最优.由模型仿真对比可知,该方法建立的功放模型结构简单、收敛快、误差小、精度高,从而验证了建模方法的有效性和可靠性.     

基于改进型PSO的模糊神经网络PM2.5浓度预测

为科学合理地预测大气污染物PM2.5颗粒物浓度变化规律,分析PM2.5颗粒物浓度变化历史数据,综合判断外部条件(温度、风速、天气状况)和内部条件(其它污染物的浓度)对PM2.5颗粒物浓度变化的影响.采用一种改进型PSO优化的模糊神经网络,将粒子群算法与模糊神经网络进行融合,发挥PSO算法全局寻优的特点,预测PM2.5颗粒物浓度的变化规律.对某市2013年PM2.5颗粒物浓度进行预测和验证,验证结果表明,该算法具备良好的预测精度.     

A hybrid particle swarm optimization approach for clustering and classification of datasets

This paper introduces a new hybrid cluster validity method based on particle swarm optimization, for successfully solving one of the most popular clustering/classifying complex datasets problems. The proposed method for the solution of the clustering/classifying problem, designated as PSORS index method, combines a particle swarm optimization (PSO) algorithm, Rough Set (RS) theory and a modified form of the Huang index function. In contrast to the Huang index method which simply assigns a constant number of clusters to each attribute, this method could cluster the values of the individual attributes within the dataset and achieves both the optimal number of clusters and the optimal classification accuracy. The validity of the proposed approach is investigated by comparing the classification results obtained for a real-world dataset with those obtained by pseudo-supervised classification BPNN, decision-tree and Huang index methods. There is good evidence to show that the proposed PSORS index method not only has a superior clustering accomplishment than the considered methods, but also achieves better classification accuracy.     

A novel hybrid particle swarm optimization for multi-UAV cooperate path planning

Applied Intelligence - The path planning of unmanned aerial vehicle (UAV) in three-dimensional (3D) environment is an important part of the entire UAV’s autonomous control system. In the...     

A hybrid particle swarm optimization and its application in neural networks

In this paper, a novel particle swarm optimization model for radial basis function neural networks (RBFNN) using hybrid algorithms to solve classification problems is proposed. In the model, linearly decreased inertia weight of each particle (ALPSO) can be automatically calculated according to fitness value. The proposed ALPSO algorithm was compared with various well-known PSO algorithms on benchmark test functions with and without rotation. Besides, a modified fisher ratio class separability measure (MFRCSM) was used to select the initial hidden centers of radial basis function neural networks, and then orthogonal least square algorithm (OLSA) combined with the proposed ALPSO was employed to further optimize the structure of the RBFNN including the weights and controlling parameters. The proposed optimization model integrating MFRCSM, OLSA and ALPSO (MOA-RBFNN) is validated by testing various benchmark classification problems. The experimental results show that the proposed optimization method outperforms the conventional methods and approaches proposed in recent literature.     

A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease

This paper presents a particle swarm optimization (PSO)-based fuzzy expert system for the diagnosis of coronary artery disease (CAD). The designed system is based on the Cleveland and Hungarian Heart Disease datasets. Since the datasets consist of many input attributes, decision tree (DT) was used to unravel the attributes that contribute towards the diagnosis. The output of the DT was converted into crisp if–then rules and then transformed into fuzzy rule base. PSO was employed to tune the fuzzy membership functions (MFs). Having applied the optimized MFs, the generated fuzzy expert system has yielded 93.27% classification accuracy. The major advantage of this approach is the ability to interpret the decisions made from the created fuzzy expert system, when compared with other approaches.     

A hybrid particle swarm optimization approach for the sequential ordering problem

The sequential ordering problem is a version of the asymmetric travelling salesman problem where precedence constraints on vertices are imposed. A tour is feasible if these constraints are fulfilled, and the objective is to find a feasible solution with minimum cost.     

An intelligent watermarking method based on particle swarm optimization

Meerwald, Koidl, and Uhl (2009) pointed out that the method proposed in Lin et al. (2008) exists potential insecurity. This paper proposes an intelligent watermarking by invoking particle swarm optimization (PSO) technique in wavelet domain to overcome the revealed insecurity issue, furthermore resolve the conflict between imperceptibility and robustness of watermarking. In the proposed method, PSO is fused with the method proposed in Lin et al. (2008) (denoted SDWCQ) to avoid potentially insecurity in Lin et al. (2008). That is, the method of using the fixed block size in one subband and the permutation is unable to disguise which coefficients make up a block. The attacker can utilize the insecure property and analyze the significant difference between bipolar watermarks in Lin et al. (2008) to detect the embedded blocks, furthermore modify the significant difference, and result in unable to extract the watermark. In this paper, coefficients are randomly selected from different subbands to make up a block to promote the disguise. Performance analysis shows that the proposed algorithm obviously outperforms SDWCQ which does not use PSO.     

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.     

粒子群和人工鱼群混合优化算法

提出基于粒子群的人工鱼群混合优化算法,该算法综合利用人工鱼群算法的良好全局收敛性和粒子群算法的局部快速收敛性、易实现性等优点,克服人工鱼群算法收敛速度慢及粒子群算法后期全局收敛差的缺点,发挥了两者的优越性,并成功应用于求解具有变量边界约束的非线性的复杂函数最优化问题和求解复杂化学方程根的问题。仿真结果表明,混合粒子群算法不仅具有较好的全局收敛性能,而且具有较快的收敛速度。     

改进的粒子群模糊聚类算法

针对传统的模糊C-均值聚类算法对初始聚类中心较敏感、易陷入局部最优的缺点,将粒子群优化算法和FCM算法相结合,提出一种改进的模糊聚类算法。该算法利用粒子群算法的全局搜索能力代替FCM算法寻找初始聚类中心,使其跳出局部最优,实现模糊聚类。主要从反映数据集分类的类内紧致性程度和类间分离性程度的角度考虑,重新设计适应度函数。实验结果表明,提出的算法在聚类正确率和有效性指标上有更好的效果。     

A novel approach for optimization of convolution neural network with hybrid particle swarm and grey wolf algorithm for classification of Indian classical dances

Knowledge and Information Systems - Deep learning is the most dominant area to perform the complex challenging tasks such as image classification and recognition. Earlier researchers have been...     

基于混合粒子群优化算法的聚类分析

针对模糊C-均值聚类算法易陷入局部最优和算法收敛速度慢等问题,提出了一种新的基于混合粒子群优化的模糊C-均值聚类算法.新算法在基本粒子群优化的模糊C-均值聚类算法的基础上结合了遗传算法的交叉、变异算子及混沌优化算法,并引入逃逸算子.仿真结果表明,该算法有效地避免了通常聚类方法易出现的早熟现象,同时也具有较快的收敛速度和较高的准确度.     

A hybrid method based on krill herd and quantum-behaved particle swarm optimization

A novel hybrid Krill herd (KH) and quantum-behaved particle swarm optimization (QPSO), called KH–QPSO, is presented for benchmark and engineering optimization. QPSO is intended for enhancing the ability of the local search and increasing the individual diversity in the population. KH–QPSO is capable of avoiding the premature convergence and eventually finding the function minimum; especially, KH–QPSO can make all the individuals proceed to the true global optimum without introducing additional operators to the basic KH and QPSO algorithms. To verify its performance, various experiments are carried out on an array of test problems as well as an engineering case. Based on the results, we can easily infer that the hybrid KH–QPSO is more efficient than other optimization methods for solving standard test problems and engineering optimization problems.     

基于粒子群优化神经网络的无线定位算法

对于传统的对移动台的定位,提出了一种基于粒子群(PSO)优化神经网络的算法。这一PSO-BP算法首先利用PSO对神经网络传统的目标函数及参数进行优化,再利用改进后的BP神经网络对非视距误差(NLOS)进行修正,最后利用算法LS进行移动台的定位。仿真结果表明,该基于PSO的神经网络定位算法寻优效果稳定,预测误差小,具有可行性。