Software defect prediction using cost-sensitive neural network

The software development life cycle generally includes analysis, design, implementation, test and release phases. The testing phase should be operated effectively in order to release bug-free software to end users. In the last two decades, academicians have taken an increasing interest in the software defect prediction problem, several machine learning techniques have been applied for more robust prediction. A different classification approach for this problem is proposed in this paper. A combination of traditional Artificial Neural Network (ANN) and the novel Artificial Bee Colony (ABC) algorithm are used in this study. Training the neural network is performed by ABC algorithm in order to find optimal weights. The False Positive Rate (FPR) and False Negative Rate (FNR) multiplied by parametric cost coefficients are the optimization task of the ABC algorithm. Software defect data in nature have a class imbalance because of the skewed distribution of defective and non-defective modules, so that conventional error functions of the neural network produce unbalanced FPR and FNR results. The proposed approach was applied to five publicly available datasets from the NASA Metrics Data Program repository. Accuracy, probability of detection, probability of false alarm, balance, Area Under Curve (AUC), and Normalized Expected Cost of Misclassification (NECM) are the main performance indicators of our classification approach. In order to prevent random results, the dataset was shuffled and the algorithm was executed 10 times with the use of n-fold cross-validation in each iteration. Our experimental results showed that a cost-sensitive neural network can be created successfully by using the ABC optimization algorithm for the purpose of software defect prediction.     

An Artificial Bee Colony algorithm with guide of global & local optima and asynchronous scaling factors for numerical optimization

Artificial Bee Colony (ABC) algorithm is a wildly used optimization algorithm. However, ABC is excellent in exploration but poor in exploitation. To improve the convergence performance of ABC and establish a better searching mechanism for the global optimum, an improved ABC algorithm is proposed in this paper. Firstly, the proposed algorithm integrates the information of previous best solution into the search equation for employed bees and global best solution into the update equation for onlooker bees to improve the exploitation. Secondly, for a better balance between the exploration and exploitation of search, an S-type adaptive scaling factors are introduced in employed bees’ search equation. Furthermore, the searching policy of scout bees is modified. The scout bees need update food source in each cycle in order to increase diversity and stochasticity of the bees and mitigate stagnation problem. Finally, the improved algorithms is compared with other two improved ABCs and three recent algorithms on a set of classical benchmark functions. The experimental results show that the our proposed algorithm is effective and robust and outperform than other algorithms.     

Zig Bee技术在振动监测中的应用

提出了一种基于Zig Bee技术的无线振动监测系统,主要应用于中小型建筑结构物的监测。阐述了无线系统的各组成单元,给出了主要单元的软件和硬件设计原理与框图。在可靠的通信协议下,通过上位机软件实现了以无线方式对各远端数据采集节点的数据传输,并对本系统进行了对比实验研究。结果表明：该无线系统高效、可靠、精度高,验证了设计方案的可行性。     

带约束的清洁排班问题模型及其求解

保洁服务公司的清洁任务往往具有不同级别、不同时长和不同周期等特点,缺乏通用清洁排班问题模型,现阶段主要依赖人工排班方案,存在耗时费力且排班质量不稳定等问题.因此提出了属于NP难问题的带约束的清洁排班问题的数学模型,并使用模拟退火算法(SA)、蜂群算法(BCO)、蚁群算法(ACO)和粒子群优化算法(PSO)对该模型进行求...     

基于改进人工蜂群算法的多无人作战飞机协同航迹规划

针对多无人作战飞机(UCAV)航迹规划约束条件复杂、不确定因素多、实时性要求高的特点,提出一种基于改进的人工蜂群算法求解多UCAV协同航迹规划模型。首先构建战场空间的改进Voronoi图生成航迹优化可飞区域;然后采用混沌搜索算法来初始化航迹集合作为算法的蜜源,使其初始航迹集合能以有限的数据充分表示航迹优化可飞区域;最后对多UCAV在多种威胁环境下的航迹空间寻优进行仿真验证。仿真结果证明改进的人工蜂群算法提高了蜜源多样性和算法的收敛速度,增强了UCAV的动态战场适应能力和突发威胁应对能力。     

基于JN5148和LabVIEW的水质监测系统

提出了一种基于JN5148和LabVIEW的水质监测系统,实现了对水质多参数的实时监测,并对系统的硬件和软件分别进行了分析和设计。本系统主要采用了JN5148单芯片作为处理器,使用Zig BeePRO协议栈作为网络通信协议。利用RS—232串口使下位机与上位机相连,在上位机中采用LabVIEW软件设计了可视化的人机界面,给系统的调试和网络的检测提供了极大的方便。经过实验证明:本系统对水样的多参量采集的实时性和准确性比较高。     

增强开发能力的改进人工蜂群算法

为解决人工蜂群（ABC）算法收敛速度慢、精度不高和易于陷入局部最优等问题，提出一种增强开发能力的改进人工蜂群算法。一方面，将得出的最优解以两种方式直接引入雇佣蜂搜索公式中，通过最优解指导雇佣蜂的邻域搜索行为，以增强算法的开发或局部搜索能力；另一方面，在旁观蜂搜索公式中结合当前解及其随机邻域进行搜索，以改善算法的全局优化能力。对多个常用基准测试函数的仿真实验结果表明，在收敛速度、精度和全局优化能力等方面，所提算法总体上优于其他类似的ABC算法（例如ABC/best）和集成多种搜索策略的ABC算法（例如ABCVSS（ABC algorithm with Variable Search Strategy）和ABCMSSCE（ABC algorithm with Multi-Search Strategy Cooperative Evolutionary））。     

Hybrid bee colony optimization for examination timetabling problems

Swarm intelligence is a branch of artificial intelligence that focuses on the actions of agents in self-organized systems. Researchers have proposed a bee colony optimization (BCO) algorithm as part of swarm intelligence. BCO is a meta-heuristic algorithm based on the foraging behavior of bees. This study presents a hybrid BCO algorithm for examination timetabling problems. Bees in the BCO algorithm perform two main actions: forward pass and backward pass. Each bee explores the search space in forward pass and then shares information with other bees in the hive in backward pass. This study found that a bee decides to be either a recruiter that searches for a food source or a follower that selects a recruiter bee to follow on the basis of roulette wheel selection. In forward pass, BCO is supported along with other local searches, including the Late Acceptance Hill Climbing and Simulated Annealing algorithms. We introduce three selection strategies (tournament, rank and disruptive selection strategies) for the follower bees to select a recruiter to maintain population diversity in backward pass. The disruptive selection strategy outperforms tournament and rank selections. We also introduce a self-adaptive mechanism to select a neighborhood structure to enhance the neighborhood search. The proposed algorithm is evaluated against the latest methodologies in the literature with respect to two standard examination timetabling problems, namely, uncapacitated and competition datasets. We demonstrate that the proposed algorithm produces one new best result on uncapacitated datasets and comparable results on competition datasets.     

基于Boltzmann选择的改进ABFM算法

模糊C-均值（FCM）聚类算法是数据挖掘中应用广泛的一种方法,但还存在容易陷入局部极小值和对初始值敏感的缺点,为此提出了一种基于Boltzmann选择机制的改进人工蜂群的模糊C-均值聚类算法（BABFM）。该算法引入了Boltzmann选择机制代替轮盘赌的选择方式,采用小区间生成法使初始群体均匀化,使得该算法的全局寻优能力更强,有效克服了FCM算法的缺点。实验结果表明,新算法与FCM和ABFM聚类算法相比聚类效果更准确,效率更高,迭代次数更少。     

基于ABC算法的群体动画研究与应用

对群体行为的模拟一直是动画研究领域的重点,如何表现出个体运动的独立性以及群体运动的整体性是群体行为模拟的难点所在,同时传统的群体动画制作手段工作量大,制作出的效果不能满足人们的需求。针对此问题文中将人工蜂群算法应用于群体行为模拟路径规划中,并针对群体动画的特征进行修改,从而产生一种新的简单、高效的制作群体动画的方法。仿真实验表明改进后的人工蜂群算法能够真实模拟出群体在运动过程中的心里状态及群体运动行为。