利用蚁群算法生成覆盖表:探索与挖掘

覆盖表生成问题是组合测试的重要研究内容之一,目前已有许多数学方法、贪心算法、搜索算法用于求解这一问题.蚁群算法作为一种能够有效求解组合优化问题的演化搜索算法,已被应用到求解覆盖表生成问题中.已有的研究工作表明:蚁群算法适于求解一般覆盖表、变力度覆盖表生成以及覆盖表排序等问题,但算法结果与其他覆盖表生成方法相比并不具有优势.为了进一步探索与挖掘蚁群算法生成覆盖表的潜力,进行了如下4个层次的改进工作:(1)算法变种集成;(2)算法参数配置优化;(3)演化对象结构调整及演化策略改进;(4)利用并行计算优化算法时间开销.实验结果表明:通过以上4个层次的改进,蚁群算法生成覆盖表的性能有了显著提升.     

基于改进鲸鱼优化的覆盖表生成算法

为提高组合测试中覆盖表生成效率,基于覆盖表生成的离散性,提出一种改进的鲸鱼优化算法。该算法首先利用编码转换的思想,将鲸鱼个体连续运动方式编码为适用于覆盖表的离散方式;其次,在算法的开发与搜索阶段加入迭代演化算子,以提高算法的全局搜索能力;最后,针对覆盖表生成中算法本身的局限问题,使用平均海明距离跳出局部最优,并通过约束求解器和惩罚函数法增加约束处理机制,以提高算法实际应用能力。实验结果表明,与其它已有算法相比,所提出的算法在覆盖表生成规模上具有更好的优势。     

基于双禁忌对象的并行禁忌搜索算法

禁忌搜索算法是解决组合优化问题的一种主要方法,是克服NP完全问题的一个有效途径。随着计算网格的发展,将禁忌搜索算法引入到这种分布式并行计算环境中,具有广泛的应用价值。提出了一个基于双禁忌对象的禁忌搜索算法,在此算法的基础上,利用并行化分散搜索策略来提高算法的求解精度。实验结果表明该并行禁忌搜索算法性能较高。     

覆盖表生成的遗传算法配置参数优化

覆盖表生成是组合测试的关键问题,很多数学方法、贪心算法以及演化搜索方法等被应用于生成各种覆盖表.针对演化搜索方法的性能受到方法本身配置参数影响很大这一实际问题,文中以二维覆盖表生成为实例,系统地对典型的演化搜索方法——遗传算法的种群规模、进化代数、交叉概率、变异概率以及遗传算法的变种算法等因素进行探索,设计了pair-wise法、Base choice法和爬山法3条实验路线探索遗传算法的这些配置参数及其相互作用对算法生成二维覆盖表效果的影响,并回答两个问题:对于特定二维覆盖表生成问题,是否存在遗传算法的最优参数配置;对于一般的二维覆盖表生成问题,是否存在通用的遗传算法最优参数配置.     

混合禁忌搜索算法求解关联运输调度问题

考虑到实际生活中车辆受发车时间限制以及道路路况影响运输成本等因素,建立了带客户软时间窗、车场硬时间窗、多车型、道路路况等约束的关联运输调度问题模型.结合禁忌搜索与遗传算法的优势,构造了混合禁忌搜索算法,以通过构造多个初始解来增大搜索空间;设计了两种禁忌表,分别为局部禁忌表和全局禁忌表,这不仅能加快寻优速度,还可以摆脱对单个解的依赖;将禁忌搜索生成的优化解作为遗传算法的初始解,可以加快寻优速度;自适应调整禁忌表长度可以避免早熟收敛;提取核心路径便于进行后期优化,relocate算子能减少路径网络回路数目.对实例进行的仿真表明,提出的IVRP优于一般的VRP,可节约大量成本,且提出的算法在收敛速度和寻优结果两方面都优于遗传算法和禁忌搜索算法.由3种算法求解得到的总成本、总里程及收敛时间的标准差体现出该算法的稳定性比另外两种算法的好.     

覆盖表生成的粒子群算法:参数优化和自适应算法

组合测试是一种能有效检测由参数间相互作用所引发错误的软件测试方法,覆盖表的生成是该研究领域的一个重要问题.目前,很多方法已被应用于覆盖表生成,基于演化搜索的粒子群算法尽管能得到较优的解,但其性能容易受到配置参数的影响.本文首先使用试验设计的方法,对不同覆盖表生成的算法参数进行优化,系统分析了参数对算法性能的影响.同时,考虑到对不同的覆盖表,最优的算法参数往往不同,因此进一步提出了一种适用于覆盖表生成的自适应粒子群算法.实验结果表明,在一定的参数取值范围内粒子群算法都能获得较好的结果,且不存在一组对任意覆盖表都能有最优性能的算法参数.通过参数调优,能使粒子群算法获得比已有结果规模更小的覆盖表,同时,与经过参数调优后的算法相比,自适应粒子群算法在大部分情况下有更好的性能.     

覆盖表生成的可配置贪心算法优化

覆盖表生成是组合测试研究的关键问题之一,其中,贪心算法因为速度快、生成的覆盖表规模小而得到人们的青睐.人们提出了很多基于不同策略的贪心算法,其中,多数算法可以归结到一个统一的算法框架,即形成一个可配置贪心算法,从该框架又可以衍生出很多新的算法.如何科学地配置优化受多个因素影响的算法框架、有效生成覆盖表是一个新的挑战.针对具有6个决策点的贪心算法框架,设计了3条不同的实验路线,系统地探索各个决策点以及它们之间相互作用对生成覆盖表规模的不同影响,寻找最佳配置,从而可以有效地生成规模更小的覆盖表,为覆盖表生成的贪心算法的设计和优化提供理论和实践基础.     

一类货运车辆调度问题的混合禁忌搜索算法

研究了一类货运车辆调度问题 :带时间窗口车辆装卸货问题 .首先给出了该问题的数学描述 ,通过引入快速局部搜索算法来加快禁忌搜索速度 ,提出了一种求解该问题的混合禁忌搜索算法 ,可以大大减少算法的运行时间而不影响解的质量 ,最后利用两个具有现实规模和复杂度的实例来测试 .结果表明 :本文提出的混合禁忌搜索算法是求解该类货运车辆调度问题的有效、快速算法 .     

带作业范围约束的岸桥调度模型及其算法设计

受电缆线坑位置与缆线长度的限制,岸桥作业只能在一定的横向移动范围之内。考虑到这一现实要求,结合岸桥作业禁止跨越与安全距离等特有约束,以最小化装卸作业的makespan为目标,构建了新的岸桥作业调度混合整数规划模型。针对问题的NP-hard特性,设计了一种混合模拟退火算法,运用启发式算法生成质量较高的初始解,结合遗传算法的变异运算生成邻域新解,增强了解的多样性,引入禁忌搜索算法的禁忌表操作,避免了循环搜索,提高了求解效率。大规模实验结果表明所建立的模型是有效的,算法的求解质量与效率明显优于标准模拟退火算法与禁忌搜索算法。当实验规模逐渐增大时,与LINGO软件相比,算法在求解效率方面的优势越来越明显。     

基于禁忌搜索的QoS路由算法

多约束的QoS路由问题是NP完全问题,该文将禁忌搜索算法引入多约束QoS路由计算中,提出了一种基于禁忌搜索的QoS路由算法QoS_TS。该算法通过设置长期记忆禁忌表和短期记忆禁忌表以及有效的评价函数,保证了算法实现过程中多样化的有效搜索。文章给出了算法实现的具体流程。实验仿真表明,该算法具有较高的搜索效率和较快的收敛性,通过该算法得到的路由不但满足QoS约束要求,同时可以均衡链路负载,减少路由拥塞。     

A GES/TS algorithm for the job shop scheduling

The job shop scheduling problem is a difficult combinatorial optimization problem. This paper presents a hybrid algorithm which combines global equilibrium search, path relinking and tabu search to solve the job shop scheduling problem. The proposed algorithm used biased random sampling to have a better covering of the solution space. In addition, a new version of N6 neighborhood is applied in a tabu search framework. In order to evaluate the algorithm, comprehensive tests are applied to it using various standard benchmark sets. Computational results confirm the effectiveness of the algorithm and its high speed. Besides, 19 new upper bounds among the unsolved problems are found.     

基于图染色问题的混合优化算法

为了提高图染色算法的寻优能力和收敛速度,结合禁忌搜索算法和遗传算法的优缺点,提出了一种混合优化算法（GA-HM）。该算法利用遗传算法生成初始解,将染色元素分到不同的色集中,然后通过禁忌算法进行变领域搜索来更新顶点染色。实验结果表明,GA-HM对求解相同的目标解具有更好的全局最优性和收敛性。     

Scheduling unrelated parallel machines with sequence-dependent setups

A methodology for minimizing the weighted tardiness of jobs in unrelated parallel machining scheduling with sequence-dependent setups is presented in this paper. To comply with industrial situations, the dynamic release of jobs and dynamic availability of machines are assumed. Recognizing the inherent difficulty in solving industrial-size problems efficiently, six different search algorithms based on tabu search are developed to identify the best schedule that gives the minimum weighted tardiness. To enhance both the efficiency and efficacy of the search algorithms, four different initial solution finding mechanisms, based on dispatching rules, are developed. While there is no evidence of identifying solutions of better quality by employing a specific initial solution finding mechanism, the use of a specific search algorithm led to identifying solutions of better quality or that required lower computation time, but not both. Based on the extensive statistical analysis performed, the search algorithm with short-term memory and fixed tabu list size is recommended for solving small size problems, while that with long-term memory and minimum frequency for solving medium and large size problems, combined with fixed tabu list size for the former and variable tabu list size for the latter.     

Extended local search algorithm based on nonlinear programming for two-dimensional irregular strip packing problem

This paper presents an extended local search algorithm (ELS) for the irregular strip packing problem. It adopts two neighborhoods, swapping two given polygons in a placement and placing one polygon into a new position. The local search algorithm is used to minimize the overlap on the basis of the neighborhoods mentioned above and the unconstrained nonlinear programming model is adopted to further minimize the overlap during the search process. Moreover, the tabu search algorithm is used to avoid local minima, and a compact algorithm is presented to improve the result. The results of standard test instances indicate that when compared with other existing algorithms, the presented algorithm does not only show some signs of competitive power but also updates several best known results.     

Fine-tuning a Tabu Search Algorithm with Statistical Tests

Tabu Search is a metaheuristic that has proven to be very effective for solving various types of combinatorial optimization problems. To achieve the best results with a tabu search algorithm, significant benefits can sometimes be gained by determining preferred values for certain search parameters such as tabu tenures, move selection probabilities, the timing and structure of elite solution recovery for intensification, etc. In this paper, we present and implement some new ideas for fine-tuning a tabu search algorithm using statistical tests. Although the focus of this work is to improve a particular tabu search algorithm developed for solving a telecommunications network design problem, the implications are quite general. The same ideas and procedures can easily be adapted and applied to other tabu search algorithms as well.     

Flexible rerouting schemes for reconfiguration of multiprocessor arrays

In a multiprocessor array, some processing elements (PEs) fail to function normally due to hardware defects or soft faults caused by overheating, overload or occupancy by other running applications. Fault-tolerant reconfiguration reorganizes fault-free PEs to a new regular topology by changing the interconnection among PEs. This paper investigates the problem of constructing as large as possible logical array with short interconnects from a physical array with faults. A flexible rerouting scheme is developed to improve the efficiency of utilizing fault-free PEs. Under the scheme, two efficient reconfiguration algorithms are proposed. The first algorithm is able to generate the maximum logical array (MLA) in linear time. The second algorithm reduces the interconnect length of the MLA, and it is capable of producing nearly optimal logical arrays in comparison to the lower bound of the interconnect length, that is also proposed in this paper. Experimental results validate the efficiency of the flexible rerouting schemes and the proposed algorithms. For 128×128 host arrays with 30% unavailable PEs, the proposed approaches improve existing algorithm up to 44% in terms of logical array size, while reducing the interconnection redundancy by 49.6%. In addition, the proposed algorithms are more scalable than existing approaches. On host arrays with 50% unavailable PEs, our algorithms can produce logical arrays with harvest over 56% while existing approaches fail to construct a feasible logical array.     

求解动态车辆调度问题的混合禁忌搜索算法

对带时间窗的动态车辆调度问题进行分析,引入虚拟点和时间轴概念,建立基于时间轴的动态车辆调度模型,并提出基于C-W节约法和禁忌搜索的混合禁忌搜索算法进行求解.算法中使用动态方法构造候选解和动态禁忌长度的选取策略来提高算法的收敛速度,最后通过测试实例验证了该混合算法解决动态车辆调度问题的有效性和可行性.     

基于改进模拟退火算法的登机口分配问题

为研究新建卫星厅对中转旅客的航班衔接的影响,分析中转旅客的换乘紧张程度,提高机场资源利用效率,本文对登机口分配问题进行研究.在最小化登机口使用个数的前提下,考虑了中转旅客的换乘紧张度,建立了飞机-登机口分配0-1整数规划模型.为改善传统启发式算法的搜索能力,本文结合变邻域搜索的邻域构造思想,综合利用集束搜索和模拟退火算法的优势,提出了基于集束搜索的改进型模拟退火算法,并借助Java语言进行编程求解.结果表明:与禁忌搜索算法、变邻域搜索算法和经典蚁群算法相比,本文所提出算法的优化效果较好.