基于改进蚁群算法的软件测试方法

为了降低软件的测试成本,提高软件测试效率,提出一种基于改进蚁群算法的软件测试方法.将Markov决策模型应用到软件测试过程当中,采用测试用例约简技术对测试用例集进行简化,利用贪心算法求得的较优解增强蚁群算法初始时刻信息素,通过改进的蚁群算法求得最优解,并进行仿真分析.仿真结果表明:改进的测试方法比采用基本蚁群算法的测试方法求得解更优,说明改进的测试方法可以使搜索时间更短,并可降低软件的测试成本.     

Research on Data Routing Model Based on Ant Colony Algorithms

Improved traditional ant colony algorithms,a data routing model used to the data remote exchange on WAN was presented.In the model,random heuristic factors were introduced to realize multi-path search.The updating model of pheromone could adjust the pheromone concentration on the optimal path according to path load dynamically to make the system keep load balance.The simulation results show that the improved model has a higher performance on convergence and load balance.     

基于超声波衰减效应的悬移质粒径分布反演

粒径分布是两相流的一项重要参数,为了实现其准确快速的测量,本文提出了一种超声波衰减效应与人工蜂群反演算法相结合的粒径分布测量方法。设计了基于聚焦式超声波传感器的悬移质参数测量系统,用于获得超声衰减信号并得到有效的实验衰减系数。根据理论声衰减模型求得理论衰减系数,构造理论衰减系数与实验衰减系数的误差函数作为目标函数。引入人工蜂群算法,优化目标函数,通过反演获得最优粒径分布。实验分别对三种不同分布的悬移质样本进行测量并采用筛分法作为对照实验,进行误差因素分析。结果表明,在实验范围内,该方法有较高的可行性与准确性,可为自由水体中悬移质粒径分布的测量提供一条新的思路。     

基于混合智能算法的电力变压器故障诊断研究

最小二乘双支持向量机算法(LSTSVM)具有训练样本速度快、二分类效率高的特点,在电力系统电力变压器故障诊断中占有独特优势。单一的智能算法诊断自己片面,很难全面进行故障诊断,因此多采用复合智能算法。在LS-TSVM模型基础上引入蚁群算法,利用蚁群算法强大的搜索能力进行寻优计算,结合二叉树构建的LS-TSVM模型可对变压器故障进行全面诊断。通过实际的算例进行仿真,结果表明,混合智能故障诊断方法不仅准确率高,准确度也比传统ANN模型有所提高,证明了该算法模型的有效性和实用性。     

面向电力物联网信息感知的低压电力线与微功率无线通信融合方法

为提高电力物联网信息感知层面的覆盖范围和可靠性,提出一种构建低压电力线与微功率无线通信跨层融合网络(CPW)的方法。首先建立CPW的统一介质访问控制(MAC)层模型,为实现CPW网络层的融合提供基础支持;然后提出一种结合布朗运动与局部收敛次数控制的改进蚁群算法,完成了CPW的组网过程;对CPW的子业务流进行分配,并提出业务分配中的误码率需求因子,实现了低压电力线与微功率无线通信网络的跨层融合。仿真结果表明,该跨层融合网络的通信链路服务质量优于电力线与无线双模或级联通信网络,用户可以根据不同业务设置相应的误码率需求因子,以兼顾通信链路质量与网络负载均衡,保障CPW的高效性和可靠性。     

基于防御雷达监控的多无人机协同侦察优化模型

针对多无人机协同侦察路径优化问题,构建一种分步求解有防御雷达监控的多无人机协同侦察优化模型.通过聚类分析的方法对待侦察目标点进行目标群的划分,运用TSP算法求出目标群内各目标点之间的最优侦察路径并对无人机进行任务分配.根据相关约束条件,建立以无人机在敌方雷达监控范围内的滞留时间最短为目标的MMTSP 优化模型.运用蚁群算法对所建模型进行求解,并进行实例分析.分析结果表明:该模型和方法具有可行性和有效性,可为多无人机协同侦察提供参考.     

Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor

In this paper, Antlion algorithm optimized Fuzzy PID supervised on-line Recurrent Fuzzy Neural Network based controller is proposed for the speed control of Brushless DC motor. Learning parameters of the supervised on-line recurrent fuzzy neural network controller, i.e., learning rate (η), dynamic factor (α), and number nodes (N_i) are optimized using Genetic algorithm, Particle Swarm optimization, Ant colony optimization, Bat algorithm, and Antlion algorithm. The proposed controller is tested with different operating conditions of the Brushless DC motor, such as varying load conditions and varying set speed conditions. The time domain specifications such as rise time, overshoot, undershoot, settling time, recovery time, and steady state error and also integral performance indices such as root mean square error, integral of absolute error, integral of squared error, and integral of time multiplied absolute error are measured and compared for above optimized controller. Simulation results show Antlion algorithm optimized Fuzzy PID supervised on-line recurrent fuzzy neural network based controller has proved to be superior than other considered controllers in all aspects. In addition, the experimental verification of proposed control system is presented to test the effectiveness of the proposed controller with different operating conditions of the Brushless DC motor.     

基于降维与搜索的网架重构

针对寻优搜索节点较多、线路较多的复杂大电力系统时计算维数过大的问题,提出了一种基于降维与搜索的网架重构方案。该方案结合优化蚁群算法与离散粒子群算法(DPSO)以搜索目标网架。首先,采用优化蚁群算法搜索一条主干线路,降低后续寻优的维数。然后,基于已经得到的主干线路,采用离散粒子群算法对电网的剩余部分进行搜索。利用已并网发电机组提供的发电功率,以考虑重要负荷的综合负荷恢复量最大为目标函数进行寻优,得到满足拓扑连通性和安全、稳定运行约束的目标网架。最后,以IEEE118节点系统和湖北电网部分地区为算例,验证了所提方法的正确性和有效性。     

Ant colony optimisation of spatial steel structures under static and earthquake loading

Optimisation is the process of trying to find out the best possible solution to any problem satisfying constraints. Soft computing is the class of methods which have been inspired by the biological computational methods and nature's problem-solving strategies. Currently, these methods include neural networks, evolutionary computational models such as genetic algorithms, random cost and linguistic models such as fuzzy logic. Ant colony optimisation (ACO) is one such method applied for large engineering combinatorial optimisation problems. A design procedure utilising an ACO technique is developed for discrete optimisation of reticulated steel space trusses. The ACO algorithm is motivated by the analogy with natural phenomena, in particular the ability of a colony of ants to ‘optimise’ their collective endeavours. In this paper, the computational implementation of ACO is presented in a structural design context. The objective function considered is the total weight/cost of the structure subjected to material and performance constraints in the form of stress and deflection limits. In the case of reticulated space trusses, the design variables are the cross-sectional areas of members belonging to various groups. The objective function and constraints are obtained by using structural analysis package FEAST (Anonymous, 1995. FEAST user manual. Trivandrum, India: SEG, SDS Group, ISRO, VSSC) in case of structures subjected to static loading and SAP90 (Anonymous, 1990. SAP90, ETABS, SAFE – computer software for structural and earthquake engineering. Berkeley, CA: Computers and Structures) for earthquake loading for reticulated steel space trusses. The numerical examples presented demonstrate the computational advantage of the ACO for large-scale optimisation problems.     

Hybrid intelligent inference model for enhancing prediction accuracy of scour depth around bridge piers

Bridge-pier scouring is a main cause of bridge failures. Thus, accurately predicting the scour depth around bridge piers is critical, both to specify adequate depths for new bridge foundations and to assess/monitor the safety of existing bridges. This study proposes a novel artificial intelligence (AI) model, the intelligent fuzzy radial basis function neural network inference model (IFRIM), to estimate future scour depth around bridge piers. IFRIM is a hybrid of the radial basis function neural network (RBFNN), fuzzy logic (FL), and the artificial bee Cclony (ABC) algorithm. In the IFRIM, FL is used to handle the uncertainties in input information, RBFNN is used to handle the fuzzy input–output mapping relationships, and the ABC search engine employs optimisation to identify the most suitable tuning parameters for RBFNN and FL based on minimal error estimation. A 10-fold cross-validation method finds that the IFRIM model achieves at least 21% and 14.5% reductions in root mean square error and mean absolute error values, respectively, compared with other AI techniques. Study results support the IFRIM as a promising new tool for civil engineers to predict future scour depth around bridge piers.