基于PSO的多目标测试用例预优化

随着软件规模的增大,在软件回归测试中,重复执行庞大的全部测试用例集已不再现实。在这种情况下,对测试用例集进行预处理就尤为重要。测试用例预优化是寻找最佳测试用例执行序列的一种技术。在实际的软件回归测试中,基于多目标的测试用例优化技术已逐步取代了单目标优化;应用进化算法解决多目标测试用例预优化是当前研究的热点。但由于进化算法主要是基于种群进行遗传迭代,种群间的交互机制相对复杂,算法的执行效率会随着种群及测试用例集规模的增大而显著下降。针对上述情况,提出了一种基于粒子群优化算法(PSO)的测试用例预优化方法,设计了粒子的表示和状态更新方式,研究了不同粒子更新方式和迭代次数及粒子群大小对多目标测试用例预优化结果的影响。实验结果显示,同基于NSGA-Ⅱ的方法相比,所提方法的执行效率显著提高,可以解决实际回归测试中的多目标测试用例预优化问题。     

基于序优化蚁群算法的成对交互测试用例集生成

为了弥补蚁群算法在评价测试用例集质量方面的缺陷,应用基于序优化蚁群算法生成优先成对交互测试用例集。在生成测试用例时,采用one-test-at-a-time策略,通过序优化蚁群算法生成涵盖更多总增益的测试用例集,对信息素更新采用分阶段方式。仿真实验表明该算法在解的质量和收敛速度方面优于基本蚁群算法。     

基于多优化目标的软件测试用例约简方法研究

软件测试是软件工程的一个重要组成部分,其目标是能够及时发现软件中的错误,确保软件高质量。测试用例是软件测试的基础,覆盖度较高且精简的测试用例集可以提高测试效率和降低成本。软件测试覆盖标准较多,一个好的测试用例评价指标也存在多种,为了能够在约简测试用例集规模的同时获取较高的测试能力,本文提出了一种基于多优化目标的测试用例集约简算法,该算法旨在根据测试用例需求,构建多优化目标的测试用例模型,使用该模型获取一个最优解的测试用例子集,使用最小化用例集方法最小化测试用例,迭代执行直到测试用例集覆盖所有的测试需求,实验结果表明该算法可以约简测试用例集,获取较高的综合测试效果。     

基于遗传蚁群融合算法的测试用例最小化研究

为缩减测试用例规模及降低回归测试成本,提出一种基于遗传蚁群融合算法的测试用例最小化方法。采用遗传算法进行遗传算子操作,其结果作为蚁群算法的初始信息素分布。使用蚁群算法进行蚂蚁路径转移和信息素的更新,得到最优解。实验结果证明,该方法能有效减小测试用例集规模,缩短运行时间,提高最小化效率。     

基于改进蚁群算法的软件回归测试用例集优化方法

为控制测试用例集合的冗余数据量,引进改进蚁群算法,设计针对软件回归测试的用例集优化方法。首先,建立测试用例集覆盖模型,掌握测试用例集数据信息的覆盖情况;其次,根据事件发生概率模型,以测试用例集的用例个数最小为优化目标,建立用例集优化目标函数;最后,在保证用例集满足要求的条件下,逐步缩小用例集信息的覆盖度,实现对测试用例集冗余信息的优化处理。实验结果证明,该方法可在最短迭代次数下使测试用例集达到最优,降低优化处理后测试用例集的冗余数据量。     

面向多目标测试用例优先排序的蚁群算法信息素更新策略

针对蚁群算法在求解多目标测试用例优先排序（MOTCP）时收敛速度缓慢、易陷入局部最优的问题,提出一种基于上位基因段（ETS）的信息素更新策略。利用测试用例序列中ETS可以决定适应度值的变化,选取ETS作为信息素更新范围,再根据ETS中测试用例间的适应度增量和测试用例的执行时间更新路径上的信息素值。为进一步提升蚁群算法求解效率、节省蚂蚁依次访问测试用例序列的时间,优化的蚁群算法还通过估算ETS长度重新设置蚂蚁遍历测试用例的搜索终点。实验结果表明,与优化前的蚁群算法及NSGA-Ⅱ相比,优化后的蚁群算法能提升求解MOTCP问题时的收敛速度,获得更优的Pareto解集。     

基于回归测试模型的用例集的优化方法研究

软件回归测试中不仅重用原有的测试用例,还要补充生成新的用例来满足系统的新功能和特征.本文针对回归测试模型,在合并原有用例集和新增用例集的基础上,根据测试需求的关系优化测试需求,然后采用启发式算法优化用例集.实例分析证明,该方法可以有效的缩减回归测试用例集的规模,大幅度降低了回归测试的费用.     

基于蚁群算法的测试用例集最小化研究

测试用例集最小化的目的是用尽可能少的测试用例充分测试给定的被测目标。把每个待测用例抽象成独立的节点,通过构造虚拟蚁群以及采用启发信息的动态更新,提出一种新的基于蚁群算法的测试用例集最小化方法及具体实现步骤。并编写算法,运行仿真程序对基于蚁群算法的测试用例集最小化方法进行验证,对实验结果的分析证明了该算法的可行性和有效性。     

面向CPU+GPU异构计算的多目标测试用例优先排序

测试用例优先排序是一种基于整个测试用例集以寻找最优测试用例执行序列的软件回归测试技术.由于其能够尽早地发现错误,同时应用灵活度高、不会漏掉重要测试用例等,在实际软件测试过程中可以有效提高测试效率.多目标测试用例优化排序是寻找同时覆盖多个测试准则的用例执行序列,通常采用演化算法优化求解,但执行时间较长,严重影响了在实际软件测试中的应用.采用先进的GPU图形卡通用并行计算技术,提出了面向CPU+GPU异构计算下的多目标测试用例优先排序技术,在NSGA-II算法中,实现了基于序列编码的适应度函数计算和交叉操作的GPU并行计算,在近6万行有效代码的工业界开源程序上实现了30倍的计算效率提升.同时,实验验证了不同并行策略的计算加速比,提出了切实可行的CPU+GPU异构计算模式,并提供了相应的原形工具.     

基于次模函数最大化的测试用例集约简

随着软件回归测试规模的不断增大和成本的不断增加,测试用例集约简对于提高软件的回归测试效率显得愈发重要.在选取测试用例子集时,需考虑该子集的代表性和多样性,并采用一个有效的算法来求解.针对该测试用例集约简问题,文中提出了一种基于次模函数最大化的算法SubTSR.尽管引入的离散优化问题是NP-hard问题,但文中利用其目标函数的次模性,采用启发式贪心搜索,求得有近似度保证的次优解.在15个数据集上对SubTSR算法与其他测试用例集约简算法展开实验,针对平均错误检出率、错误检测损失率、首次错误检出位等指标,尝试改变LDA处理中的主题个数以及衡量测试用例相似度的距离,以验证SubTSR算法的有效性.实验结果表明,SubTSR算法在错误检出性能上较其他算法有着较大提升,且在多个数据集上的表现保持相对稳定.在主题个数变化引起文本表示变化时,采用曼哈顿距离的SubTSR算法的性能相较其他算法仍能保持相对稳定.     

Formulation and parameter selection of multi-objective deterministic particle swarm for simulation-based optimization

Global derivative-free deterministic algorithms are particularly suitable for simulation-based optimization, where often the existence of multiple local optima cannot be excluded a priori, the derivatives of the objective functions are not available, and the evaluation of the objectives is computationally expensive, thus a statistical analysis of the optimization outcomes is not practicable. Among these algorithms, particle swarm optimization (PSO) is advantageous for the ease of implementation and the capability of providing good approximate solutions to the optimization problem at a reasonable computational cost. PSO has been introduced for single-objective problems and several extension to multi-objective optimization are available in the literature. The objective of the present work is the systematic assessment and selection of the most promising formulation and setup parameters of multi-objective deterministic particle swarm optimization (MODPSO) for simulation-based problems. A comparative study of six formulations (varying the definition of cognitive and social attractors) and three setting parameters (number of particles, initialization method, and coefficient set) is performed using 66 analytical test problems. The number of objective functions range from two to three and the number of variables from two to eight, as often encountered in simulation-based engineering problems. The desired Pareto fronts are convex, concave, continuous, and discontinuous. A full-factorial combination of formulations and parameters is investigated, leading to more than 60,000 optimization runs, and assessed by three performance metrics. The most promising MODPSO formulation/parameter is identified and applied to the hull-form optimization of a high-speed catamaran in realistic ocean conditions. Its performance is finally compared with four stochastic algorithms, namely three versions of multi-objective PSO and the genetic algorithm NSGA-II.     

Real-time dynamic optimization of batch systems

In this paper a methodology for designing and implementing a real-time optimizing controller for batch processes is proposed. The controller is used to optimize a user-defined cost function subject to a parameterization of the input trajectories, a nominal model of the process and general state and input constraints. An interior point method with penalty function is used to incorporate constraints into a modified cost functional, and a Lyapunov based extremum seeking approach is used to compute the trajectory parameters. The technique is applicable to general nonlinear systems. A precise statement of the numerical implementation of the optimization routine is provided. It is shown how one can take into account the effect of sampling and discretization of the parameter update law in practical situations. A simulation example demonstrates the applicability of the technique.     

Multiobjective optimization of trusses using genetic algorithms

In this paper we propose the use of the genetic algorithm (GA) as a tool to solve multiobjective optimization problems in structures. Using the concept of min–max optimum, a new GA-based multiobjective optimization technique is proposed and two truss design problems are solved using it. The results produced by this new approach are compared to those produced by other mathematical programming techniques and GA-based approaches, proving that this technique generates better trade-offs and that the genetic algorithm can be used as a reliable numerical optimization tool.     

An integrated approach to topology,sizing, and shape optimization

Topology optimization has become very popular in industrial applications, and most FEM codes have implemented certain capabilities of topology optimization. However, most codes do not allow simultaneous treatment of sizing and shape optimization during the topology optimization phase. This poses a limitation on the design space and therefore prevents finding possible better designs since the interaction of sizing and shape variables with topology modification is excluded. In this paper, an integrated approach is developed to provide the user with the freedom of combining sizing, shape, and topology optimization in a single process.     

多元插值全局优化算法及其应用

本文介绍一种多元插值逼近和动态搜索轨迹相结合的全局优化算法.该算法大大减少了目标函数计算次数,寻优收敛速度快,算法稳定,且可获得全局极小,有效地解决了大规模非线性复杂动态系统的参数优化问题.一个具有8个控制参数的电力系统优化控制问题,采用该算法仅访问目标函数78次,便可求得最优控制器参数。     

Effects of dominant wolves in grey wolf optimization algorithm

Bio-inspired computation is one of the emerging soft computing techniques of the past decade. Although they do not guarantee optimality, the underlying reasons that make such algorithms become popular are indeed simplicity in implementation and being open to various improvements. Grey Wolf Optimizer (GWO), which derives inspiration from the hierarchical order and hunting behaviours of grey wolves in nature, is one of the new generation bio-inspired metaheuristics. GWO is first introduced to solve global optimization and mechanical design problems. Next, it has been applied to a variety of problems. As reported in numerous publications, GWO is shown to be a promising algorithm, however, the effects of characteristic mechanisms of GWO on solution quality has not been sufficiently discussed in the related literature. Accordingly, the present study analyses the effects of dominant wolves, which clearly have crucial effects on search capability of GWO and introduces new extensions, which are based on the variations of dominant wolves. In the first extension, three dominant wolves in GWO are evaluated first. Thus, an implicit local search without an additional computational cost is conducted at the beginning of each iteration. Only after repositioning of wolf council of higher-ranks, the rest of the pack is allowed to reposition. Secondarily, dominant wolves are exposed to learning curves so that the hierarchy amongst the leading wolves is established throughout generations. In the final modification, the procedures of the previous extensions are adopted simultaneously. The performances of all developed algorithms are tested on both constrained and unconstrained optimization problems including combinatorial problems such as uncapacitated facility location problem and 0-1 knapsack problem, which have numerous possible real-life applications. The proposed modifications are compared to the standard GWO, some other metaheuristic algorithms taken from the literature and Particle Swarm Optimization, which can be considered as a fundamental algorithm commonly employed in comparative studies. Finally, proposed algorithms are implemented on real-life cases of which the data are taken from the related publications. Statistically verified results point out significant improvements achieved by proposed modifications. In this regard, the results of the present study demonstrate that the dominant wolves have crucial effects on the performance of GWO.     

云搜索优化算法

本文将云的生成、动态运动、降雨和再生成等自然现象与智能优化算法的思想融合,建立了一种新的智能优化算法-云搜索优化算法(CSO)。生成与移动的云可以弥漫于整个搜索空间,这使得新算法具有较强的全局搜索能力;收缩与扩张的云团在形态上会有千奇百态的变化,这使得算法具有较强的局部搜索能力;降雨后产生新的云团可以保持云团的多样性,这也是使搜索避免陷入局优的有效手段。实验表明,基于这三点建立的新算法具有优异的性能,benchmark函数最优值的计算结果以及与已有智能优化算法的比较展现了新算法精确的、稳定的全局求解能力。     

Value Creation in a Network: The Role of Pricing and Revenue Optimization and Enterprise Profit OptimizationTM

The Internet has created a virtual upheaval in the structural features of the supply and demand chains for most businesses. New agents and marketplaces have surfaced. The potential to create value and enhance profitable opportunities has attracted both buyers and sellers to the Internet. Yet, the Internet has proven to be more complex than originally thought. With information comes complexity: the more the information in real time, the greater the difficulty in interpretation and absorption. How can the value-creating potential of the Internet still be realized, its complexity notwithstanding? This paper argues that with the emergence of innovative tools, the expectations of the Internet as a medium for enhanced profit opportunities can still be realized. Creating value on a continuing basis is central to sustaining profitable opportunities. This paper provides an overview of the value creation process in electronic networks, the emergence of the Internet as a viable business communication and collaboration medium, the proclamation by many that the future of the Internet resides in “embedded intelligence”, and the perspectives of pragmatists who point out the other facet of the Internet—its complexity. The paper then reviews some recent new tools that have emerged to address this complexity. In particular, the promise of Pricing and Revenue Optimization (PRO) and Enterprise Profit Optimization^TM (EPO) tools is discussed. The paper suggests that as buyers and sellers adopt EPO, the market will see the emergence of a truly intelligent network—a virtual network—of private and semi-public profitable communities.     

粒子群算法在组合优化问题上的研究与发展

粒子群优化算法是一种新兴的基于群智能搜索的优化技术。该算法简单、易实现、参数少,具有较强的全局优化能力,可有效应用于科学与工程实践中。介绍了算法的基本原理和算法在组合优化上一些改进方法的主要应用形式。最后,对粒子群算法作了一些深入分析并在此基础上对粒子群算法应用于组合优化问题做了一些总结。     

SEO技术研究

为了利用搜索引擎优化SEO（Search Engine Optimization）技术给网站带来高质量的流量并将其转化为商业利益,理解搜索引擎的算法和排名原理十分必要。通过对网站的结构优化、关键词优化、单页优化、防止被搜索引擎惩罚和挽救被惩罚网站等技术的研究,达到提高网站排名,实现网站的价值目的。