Solution for integer linear bilevel programming problems using orthogonal genetic algorithm

An integer linear bilevel programming problem is firstly transformed into a binary linear bilevel programming problem, and then converted into a single-level binary implicit programming. An orthogonal genetic algorithm is developed for solving the binary linear implicit programming problem based on the orthogonal design. The orthogonal design with the factor analysis, an experimental design method is applied to the genetic algorithm to make the algorithm more robust, statistical y sound and quickly convergent. A crossover operator formed by the orthogonal array and the factor analysis is presented. First, this crossover operator can generate a smal but representative sample of points as offspring. After al of the better genes of these offspring are selected, a best combination among these offspring is then generated. The simulation results show the effectiveness of the proposed algorithm.

An Exact Virtual Network Embedding Algorithm Based on Integer Linear Programming for Virtual Network Request with Location Constraint

Network virtualization is known as a promising technology to tackle the ossification of current Internet and will play an important role in the future network area. Virtual network embedding(VNE) is a key issue in network virtualization. VNE is NP-hard and former VNE algorithms are mostly heuristic in the literature.VNE exact algorithms have been developed in recent years. However, the constraints of exact VNE are only node capacity and link bandwidth.Based on these, this paper presents an exact VNE algorithm, ILP-LC, which is based on Integer Linear Programming(ILP), for embedding virtual network request with location constraints. This novel algorithm is aiming at mapping virtual network request(VNR) successfully as many as possible and consuming less substrate resources.The topology of each VNR is randomly generated by Waxman model. Simulation results show that the proposed ILP-LC algorithm outperforms the typical heuristic algorithms in terms of the VNR acceptance ratio, at least 15%.

A new heuristic algorithm for general integer linear programming problems

A new heuristic algorithm is proposed for solving general integer linear programming problems. In the algorithm, the objective function hyperplane is used as a cutting plane, and then by introducing a special set of assistant sets, an efficient heuristic search for the solution to the integer linear program is carried out in the sets on the objective function hyperplane. A simple numerical example shows that the algorithm is efficient for some problems, and therefore, of practical interest.

Improved Balas and Mazzola Linearization for Quadratic 0-1 Programs with Application in a New CuttingPlane Algorithm

Balas and Mazzola linearization (BML) is widely used in devising cutting plane algorithms for quadratic 0-1 programs. In this article, we improve BML by first strengthening the primal formulation of BML and then considering the dual formulation. Additionally, a new cutting plane algorithm is proposed.

A hybrid algorithm based on ILP and genetic algorithm for time-aware test case prioritization

To solve the problem of time-awarc test case prioritization,a hybrid algorithm composed of integer linear programming and the genetic algorithm(ILP-GA)is proposed.First,the test case suite which cm maximize the number of covered program entities a d satisfy time constraints is selected by integer linea progamming.Secondly,the individual is encoded according to the cover matrices of entities,and the coverage rate of program entities is used as the fitness function and the genetic algorithm is used to prioritize the selected test cases.Five typical open source projects are selected as benchmark programs.Branch and method are selected as program entities,and time constraint percentages a e 25%and 75%.The experimental results show that the ILP-GA convergence has faster speed and better stability than ILP-additional and IP-total in most cases,which contributes to the detection of software defects as early as possible and reduces the software testing costs.

求解带有阻塞限制的HFSP的MILP模型与改进回溯搜索算法

针对带有阻塞限制的不相关并行机混合流水车间调度问题,以最小化最长完工时间为目标,依据不同的建模思想,建立了求解该问题的4个混合整数线性规划(MILP)模型;鉴于混合整数线性规划不适合求解中大规模问题,提出了一种改进的回溯搜索算法以求解中大规模问题,在该算法中,引入了轮盘赌选择策略以及变邻域搜索算法,以提高算法的收敛速度以及局部搜索能力。最后,对所提MILP模型以及算法进行了对比分析,通过对具体实例的求解验证了所提MILP模型以及算法的有效性及优越性。

基于ILP和最优光路的智能电网实时虚拟拓扑模型

为了满足智能电网骨干网络的实时延迟和最优光路的相关网络要求,提出一种基于整数线性规划(ILP)的虚拟拓扑模型。该模型包括网络延迟相关目标函数和光路路由、波长分配、波长连续性、流路由和流量损失约束,采用网络流量矩阵解决相应的虚拟拓扑问题。通过对时变网络流量数据和运行约束的自适应响应,来保持最优虚拟拓扑,从而为智能电网运营提供智能化感知和网络学习。仿真结果表明所提模型可以为智能电网实现优良的网络性能。

软件定义网络中基于网络切片和ILP模型的路由方案

针对软件定义网络(SDN)中数据层的路由优化问题,提出一种基于网络切片和整数线性规划(ILP)多约束优化的路由方案。首先,根据多租户业务的链路需求,基于Kruskal算法对数据层中的链路资源进行网络切片,尽可能形成相互隔离的租户子网络。然后,在考虑链路约束和租户业务的服务质量(QoS)约束下,以最小化传输延迟为目标,构建一个ILP整数线性规划(ILP)路由优化模型,并获得最佳的路由方案。仿真结果表明,所获得的路由方案具有较少的共享链路,有效降低了链路拥塞和传输延迟。

基于ILP的电压岛驱动的多电压分配算法

为降低芯片功耗和电源布线网络的复杂度,针对可切分的布图结构,提出了一种时序约束下在后布图阶段进行多电压分配的整数线性规划模型,对功耗和电源网络进行协同优化,同时控制插入电平移位器的数目。为构建电源网络复杂度的度量模型,提出了一种时间复杂度仅为O（n）的模块相邻图构建方法来获得模块之间的相邻信息。模型中还考虑了在不同电压差的模块之间连线上插入不同延时和功耗的电平移位器,以进一步降低功耗。对5个GSRC电路的实验结果表明：所提出算法不仅可降低16.7-31.5%不等的功耗,还可将相同电压的模块有效集中在一起,形成电压岛。

基于MILP模型的PFP算法的不可能差分分析

PFP算法是借鉴国际标准PRESENT算法设计思想提出的一种轻量级分组密码算法,它使用Feistel结构,分组长度为64比特,密钥长度为80比特,迭代34轮。与以往的手动推导等方法不同,本文对PFP算法建立基于混合整数线性规划(MILP)的不可能差分计算模型,通过自动搜索研究获得了12,497个9轮(均是目前为止最长的)不可能差分区分器。并选取一个可以利用PFP算法S盒的差分传播特性的差分路径,构造出不可能差分区分器,实现了对PFP算法进行了13轮(文献中最多到9轮)不可能差分分析。这是目前为止攻击轮数最多、时间复杂度最低的一个攻击结果。

ACT、TED算法基于MILP搜索的积分分析

ACT和TED算法是近年来相继被提出的,分别基于SPN结构和Feistel结构而设计的两种轻量级分组密码算法.本文根据这两种算法不同的结构特性,分别构建了相关的基于比特可分性的MILP模型,并调用求解工具Gurobi对MILP模型进行求解.实验结果表明:ACT和TED算法都存在8、9、10轮积分区分器,另外TED算法还存在7轮积分区分器.根据所搜索到的积分区分器,实现了两种算法的11轮密钥恢复攻击方案.利用ACT算法的9轮积分区分器向后扩展2轮进行11轮的密钥恢复攻击时,攻击数据复杂度为2^(36.39),时间复杂度为2^(36.39)次11轮加密,存储复杂度为2^(20).利用TED算法的8轮积分区分器向后扩展3轮进行11轮的密钥恢复攻击时,数据复杂度为2^(60.81),时间复杂度为2^(109.54)次11轮加密,存储复杂度为2^(59).

Randomized MILP framework for Securing Virtual Machines from Malware Attacks

Cloud computing involves remote server deployments with public net-work infrastructures that allow clients to access computational resources.Virtual Machines(VMs)are supplied on requests and launched without interactions from service providers.Intruders can target these servers and establish malicious con-nections on VMs for carrying out attacks on other clustered VMs.The existing system has issues with execution time and false-positive rates.Hence,the overall system performance is degraded considerably.The proposed approach is designed to eliminate Cross-VM side attacks and VM escape and hide the server’s position so that the opponent cannot track the target server beyond a certain point.Every request is passed from source to destination via one broadcast domain to confuse the opponent and avoid them from tracking the server’s position.Allocation of SECURITY Resources accepts a safety game in a simple format as input andfinds the best coverage vector for the opponent using a Stackelberg Equilibrium(SSE)technique.A Mixed Integer Linear Programming(MILP)framework is used in the algorithm.The VM challenge is reduced by afirewall-based controlling mechanism combining behavior-based detection and signature-based virus detection.The pro-posed method is focused on detecting malware attacks effectively and providing better security for the VMs.Finally,the experimental results indicate that the pro-posed security method is efficient.It consumes minimum execution time,better false positive rate,accuracy,and memory usage than the conventional approach.

基于MILP的PICO算法差分和线性区分器的搜索

PICO算法是由Bansod等人在2016年提出的一种基于替换和排列的SPN网络的轻量级分组密码.目前针对PICO算法抵抗差分和线性分析的能力还有待进一步评估.本文基于MILP模型,首先利用不等式组对各组件的差分特征和线性掩码的传播规律进行了精细的刻画,其次针对密码算法的结构特点及S盒特性,对该模型进行了优化,缩小了搜索空间,并提出了针对PICO算法有效的两步搜索算法.最后利用该算法,搜索到了3条新的概率为2−63的21轮差分区分器,并首次搜索到了3条相关度为2−30的20轮线性区分器,为当前公开发表的最长线性区分器.

Heuristic Algorithm for Minimizing the Electricity Cost of Smart House

This framework proposes a heuristic algorithm based on LP （linear programming） for optimizing the electricity cost in large residential buildings, in a smart grid environment. Our heuristic tackles large multi-objective energy allocation problem （large number of appliances and high time resolution）. The primary goal is to reduce the electricity bills, and discomfort factor. Also, increase the utilization of domestic renewable energy, and reduce the running time of the optimization algorithm. Our heuristic algorithm uses linear programming relaxation, and two rounding strategies. The first technique, called CR （cumulative rounding）, is designed for thermostatic appliances such as air conditioners and electric heaters, and the second approach, called MCR （minimum cost rounding）, is designed for other interruptible appliances. The results show that the proposed heuristic algorithm can be used to solve large MILP （mixed integer linear programming） problems and gives a decent suboptimal solution in polynomial time.

分支定界搜索信息深度引导的电-气互联系统调度决策加速求解方法

电-气互联系统调度决策问题旨在实现天然气系统和电力系统中可调节资源的最佳配置,其精准性与高效性直接影响电-气互联系统运行的安全性与经济性。为描述可调节资源离散状态、非线性运行特性等物理性质,电-气互联系统调度决策问题中含有规模庞大的离散决策变量,模型复杂度高,使得现有依赖于商业混合整数线性规划(MILP)求解器的电力系统运筹优化技术面临“组合爆炸”的计算负担。为此,该文提出一种分支定界搜索信息深度引导的电-气互联系统调度决策加速求解方法。所提方法利用分支定界初始搜索阶段的信息构建小规模辅助MILP模型,并内嵌于分支定界搜索过程,引导剪除更多冗余搜索空间,在不损失最优性的前提下加速收敛。基于RTS-GMLC电力系统和天然气系统不同负荷水平及线性分段数下的30个算例仿真结果说明,相比于直接使用商业MILP求解器,所提方法在不损失最优性的前提下可实现平均4.20倍的加速,验证了所提方法的有效性。

基于CSA-AFSA算法的集装箱港口连续型泊位分配优化

为提升集装箱港口运营效率,笔者研究了考虑潮汐因素与泊位偏好的连续型泊位分配问题。引入了船舶时空矩形不可重叠约束和潮汐时间窗约束,构建以最小化船舶等待、延迟离港、泊位偏离以及在港期间油耗费用和最小为目标的混合整数线性规划模型;根据模型特征,采用CPLEX求解软件、鱼群算法、布谷鸟搜索算法和布谷鸟鱼群混合算法进行求解,以计划周期为36 h的20个不同规模的船舶到港数据为研究算例,通过算例求解得到符和潮汐时间窗、泊位偏好等要求的泊位分配方案。算例求解表明:算例规模较小时,CPLEX可以在较短时间内求出最优泊位分配方案;算例规模较大时,CPLEX求解时间较长,布谷鸟鱼群混合算法可以在平均3 min内求出与CPLEX差距为0.39%~4.20%的次优解;不同算法间的对比表明,布谷鸟鱼群混合算法求解能力更优,混合算法所得泊位分配方案中,乘潮船舶的进出港时刻均在潮汐高水位时段,且85%以上的船舶在偏好泊靠点200 m内接受装卸服务。

含分布电源配电网多时段线性化二阶锥松弛算法研究

针对分布式电源接入配电网影响系统电压分布和潮流分析、造成系统电能质量下降的问题,建立系统优化模型,对系统电压、潮流进行分析与优化,提出含分布式电源的配电网多时段线性化二阶锥松弛优化算法。对分布式电源接入的配电网进行潮流分析,建立分布式配电网最优潮流优化模型;对分布式配电网潮流优化模型进行简化处理,针对潮流分析中非凸非线性,提出多时段二阶锥松弛优化算法;针对电容器组、有载调压变压器的非凸非线性问题,进行分段线性化处理。仿真结果表明,提出的算法能合理调度有载调压变压器、电容器组和分布电源的出力,有效降低网损,减小电网电压偏差。

基于换电的共享电动汽车调度与服务定价

为解决共享电动汽车出行模式车辆失衡与电能补给问题,基于集充统配换电模式,考虑共享电动汽车电能补给、换电站选址与电池配送路径间的耦合关系,基于时空网络构建以最大化利润为目标的共享电动汽车调度与服务定价优化模型,并通过共享电动汽车需求-价格函数逆变换,将非线性非凸模型转换成更易求解的非线性凸模型。数值试验结果表明:协同决策换电站位置、电池配送路径、共享电动汽车调度方案与服务价格,可提高共享电动汽车出行服务商利润;基于割线的外逼近算法比基于切线的外逼近算法和拉格朗日松弛算法更适于求解所提出的模型。参数敏感度分析结果显示,潜在共享电动汽车出行需求与电能可得性都是影响共享电动汽车出行服务商利润的重要因素。

考虑潮汐和泊位偏好的连续型泊位分配优化

由于潮汐因素对船舶进出港口航道有影响,以及船舶偏离偏好泊位也对船舶处理时间有影响,所以研究连续型泊位分配问题要综合考虑潮汐与偏好泊位两个因素的影响。以计划期内所有到港船舶的总时间最小为目标,将偏好泊靠点作为约束条件构建混合整数线性规划模型,并设计模拟退火遗传混合算法进行求解。该混合算法将遗传算法的全局搜索能力与模拟退火的局部搜索能力相结合,提升了算法的搜索效率。通过算例求解验证了算法和模型的有效性。

一种基于改进遗传算法的航空集装器多目标配载优化方法

航空运输是重要的交通运输方式之一,而航空集装器配载会影响飞机飞行安全和航空公司商业利益。针对此问题,本文建立以机体轴惯性矩最小和重心偏差最小为目标的航空集装器装载的混合整数线性规划模型的方法,通过改进遗传算法中的适应度函数、选择、交叉和变异等操作提高算法的求解能力。实验结果表明,改进的方法会使重心偏差低于0.05%MAC,惯性矩减小10%以上。