基于改进遗传算法的可靠性网络优化设计

在可靠性条件约束下，使网络成本最低是网络规划NP—hard问题。从遗传算法的基本原理出发并对其进行改进，分析带有可靠性约束条件的通信网设计中的网络优化问题，这一方法的最大优点是可将其推广到求解一般带有约束的网络优化问题。而且结果表明无论是解的精度还是运算速度遗传算法都优于分枝定界法及其它启发式算法。     

基于可靠性理论的计算机通信网络分析及多目标优化

基于可靠性理论的计算机通信网络的优化问题是一个多条件约束问题,在分析计算机网络可靠性理论的基础上,结合多目标优化设计原则,分析讨论了计算机通信网络中的多目标优化模型及通过遗传算法,在给定的优化设计可靠度目标要求下,完成了对一组网络通信系统的优化分析,结果显示基于可靠性理论的计算机通信网络优化设计的算法是有效的。     

遗传算法在传感器优化配置中的应用

针对于传感器网络中传感器配置问题,在保证局部网络损坏的情况下仍能进行检测,提高检测网络的可靠性,提出了用遗传算法对其进行优化,并设计了该遗传算法的计算步骤和其相应的适应度函数程序。计算了一个实例,得出了较好的配置方式,满足了传感网络可靠性的要求,说明了用遗传算法进行传感器优化配置是可行的。     

有约束的三维RFID读写器网络规划方法

针对现有的读写器网络规划计算量大和优化困难的问题,基于微遗传算法,提出一种带有约束的网络规划方法,以优化无线射频识别读写器。运用空间交叉的方法解决种群数量较少导致的搜索能力退化问题,提高搜索能力。为降低迭代成本,校正方案包含了冗余读写器的消除操作,使得校正后的可行性染色体都是低成本染色体。实验结果表明,与典型遗传算法和粒子群优化算法相比,该方法的计算效率较高,在计算时间和标签覆盖率方面具有较大优势。     

基于遗传算法的实时QoS多播路由优化算法

讨论了多QoS约束的多播路由问题，描述了一种适应于研究实时QoS多播路由的网络模型和优化函数。提出一种基于遗传算法中的小生境技术的QoS多播树选择算法，同时优化了多QoS参数。仿真结果表明该算法收敛速度快、可靠性高，能够满足多媒体网络对实时性的要求。     

遗传算法在全终端网络可靠性优化中的应用

在计算机通信网络建设过程中,通信主干网的高可靠性一直是广大设计者和用户所关心的重大问题。分析了通信主干网的全终端可靠性优化设计问题,建立了一种新的可靠性优化模型,并利用遗传算法对该模型进行了优化求解。计算机仿真实验结果表明：遗传算法在解决此类问题上是有效的。     

采用神经网络和屏蔽遗传算法为VP网络动态分配带宽的方法

虚路径（ＶＰ）的概念已成为宽带网络中的关键技术,提出采用神经网络与屏蔽遗传算法的一种控制算法,来为宽带网络的虚路径分配带宽．该算法能根据各类业务流的到达与服务特性和网络环境,采用神经网络来估计ＶＰ子网的利用率、并应用遗传算法来自适应地为各ＶＰ子网分配最佳带宽．由于它是一个带约束的优化问题,传统的遗传算法对它不再适用．作者提出应用屏蔽遗传算法来解决该优化问题．仿真结果表明了这种动态分配方法的优越性。     

基于遗传算法的多约束QoS多播路由优化算法

随着大量新型的多媒体在高性能网络、移动网络及Internet中的应用，满足QoS约束的多播路由问题成为越来越重要，它吸引了许多爱好者．本文讨论了多约束QoS多播路由问题，主要包含延迟、延迟抖动、带宽和分组丢失率等QoS约束，文中描述了一种在动态网络环境及不确定参数下适应于研究QoS多播路由的网络模型．提出了一种在网络规模、可行性方面为Imernet、移动网络和高性能网络下基于遗传算法的多约束QoS多播路由优化算法（MQMRGA）．仿真结果表明该算法收敛速度快、可靠性高．MQMRGA为QoS多播路由提供了一种新的有效途径．     

无线传感器网络中基于遗传算法的优化覆盖机制

覆盖作为无线传感器网络应用的一个基本问题．反映了网络监测和实现目标跟踪的质量效果．针对传感器节点的高密度部署情况，研究了工作节点集选取问题．提出两种基于加权遗传算法和基于约束遗传算法的优化覆盖机制．根据生成的适值函数进行遗传算法操作．并计算传感器网络充分覆盖区域所需的近似最优工作节点集．仿真结果表明该算法能快速收敛于最优解．完成工作节点集的优化选取，从而降低网络冗余，延长网络生存时间．     

一类约束优化问题的改进遗传算法

遗传算法是一种解决非线性无约束优化问题的搜索算法，对于约束优化问题通常采用罚函数法；所提出了的改进算法很好地解决了一类带有凸约束的非红性优化问题。数值结果说明该方法效果较好。     

考虑胜任力水平的研发项目群人力资源调度

在资源受限项目调度问题中,将可更新资源进一步拓展为具有胜任力差异的人力资源,建立考虑胜任力差异的人力资源受限项目调度问题模型,该模型是对传统多模式资源约束项目调度问题（MRCPSP）更接近研发项目群实际的扩展。提出了衡量人员胜任力的参数及估算公式,以多项目总工期和总成本最小化为双目标,建立相应的数学优化模型。按双目标重要性排序,依次对工期最优及成本最优的单目标优化问题求解。根据模型的约束条件将多项目初始网络图转化为几种单项目初始网络图,利用枚举算法给出满足约束条件的可行解集,再设计基于动态规划思想的算法进行分阶段寻优。数值实验表明,考虑了胜任力差异的数学优化模型在求解质量方面具有良好性能。     

Automatic network clustering via density-constrained optimization with grouping operator

Automatic network clustering is an important technique for mining the meaningful communities (or clusters) of a network. Communities in a network are clusters of nodes where the intra-cluster connection density is high and the inter-cluster connection density is low. The most popular scheme of automatic network clustering aims at maximizing a criterion function known as modularity in partitioning all the nodes into clusters. But it is found that the modularity suffers from the resolution limit problem, which remains an open challenge. In this paper, the automatic network clustering is formulated as a constrained optimization problem: maximizing a criterion function with a density constraint. With this scheme, the established algorithm can be free from the resolution limit problem. Furthermore, it is found that the density constraint can improve the detection accuracy of the modularity optimization. The efficiency of the proposed scheme is verified by comparative experiments on large scale benchmark networks.     

多源多宿组播网络编码的可达信息率区域

为了解决多源多宿组播网络编码问题,提出了计算可达信息率区域的算法和构造线性网络编码的方法。在已有研究的基础上,把多源多宿组播网络编码问题转化为一个含有约束的单源组播网络编码问题,通过理论分析与推导,找出了各源点组播率之间的相互约束关系,进而构造了一个多目标优化模型来表征可达信息率区域的边界,提出了两种求解该多目标优化问题的方法:枚举法和基于遗传算法的多目标优化算法。从求出的Pareto边界可以导出可达信息率区域。选定了各源点的组播率后,通过求解含有约束的单源组播网络编码问题便可以构造出线性网络编码方案。仿真测试结果表明提出的方法可以求出可达信息率区域的整数点边界,并能构造线性网络编码方案。     

Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling

The expanded job-shop scheduling problem (EJSSP) is a practical production scheduling problem with processing constraints that are more restrictive and a scheduling objective that is more general than those of the standard job-shop scheduling problem (JSSP). A hybrid approach involving neural networks and genetic algorithm (GA) is presented to solve the problem in this paper. The GA is used for optimization of sequence and a neural network (NN) is used for optimization of operation start times with a fixed sequence.

After detailed analysis of an expanded job shop, new types of neurons are defined to construct a constraint neural network (CNN). The neurons can represent processing restrictions and resolve constraint conflicts. CNN with a gradient search algorithm, gradient CNN in short, is applied to the optimization of operation start times with a fixed processing sequence. It is shown that CNN is a general framework representing scheduling problems and gradient CNN can work in parallel for optimization of operation start times of the expanded job shop.

Combining gradient CNN with a GA for sequence optimization, a hybrid approach is put forward. The approach has been tested by a large number of simulation cases and practical applications. It has been shown that the hybrid approach is powerful for complex EJSSP.     

A neural network theory for constrained optimization

A variety of real-world problems can be formulated into continuous optimization problems with constraint equalities. The real-world problem here can include, for example, the traveling salesman problem, the Dido’s isoperimetric problem, the Hitchcock’s transportation problem, the network flow problem and the associative memory problem. In spite of the significance, there has not yet been developed any robust solving method that works efficiently across a broad spectrum of optimization problems. The recent Hopfield’s neural network method to solve the traveling salesman problem is a potentially promising candidate because of the efficiency due to its parallel processing. His method, however, has certain drawbacks that must be removed away before it can be qualified for an efficient, robust solving method. That is: (a) locally minimum solutions instead of globally minimum; (b) possible infeasible solutions; (c) heuristic choice of network parameters and an initial state; (d) quadratic objective functions instead of arbitrary nonlinear objective functions with arbitrary nonlinear equality constraints; and (e) unorganized mathematical formulation of the network for extension. This paper develops from the Hopfield method an efficient, robust network solving method of the continuous optimization problem with constraint equalities that resolves all the drawbacks except for (a) that has already been resolved by others. The development is mathematically rigorous and thus constitutes a solid foundation of a neural network theory for constrained optimization.     

基于误码率与中断概率约束的femtocell网络分布式功率控制

家庭基站(femtocell)网络可有效改善无线通信业务的室内覆盖性能,提高信道容量.然而,复杂的动态通信环境导致信道的不确定性,影响用户服务质量.基于此,研究双层femtocell网络在快衰落信道环境下基于误码率约束的功率控制问题;考虑信号传输的中断概率,以及服务质量指标–误码率等方面的要求,构造在此约束下的优化问题;最大化双层femtocell网络的净收益,使得网络系统的通信性能最优;通过对概率约束进行数学处理,将其转化为确定性形式,并提出分布式鲁棒优化算法对等价的确定性优化问题进行求解,从而获得最优功率分配策略.最后,通过仿真验证了所提出算法的收敛性和有效性.     

基于雁群启示的粒子群优化算法的几何约束求解

几何约束是约束求解技术中最关键的问题之一.求解一个几何约束问题的最终目的是确定几何图形中每一个几何体的具体坐标位置.几何约束问题可以等价为求解非线性方程组问题.约束问题转化为一个优化问题.本文采用基于雁群启示的粒子群优化算法来求解该问题.该算法受雁群飞行特征启发,一方面将粒子排序,每个粒子跟随其前面那个较优粒子飞行,保持了多样性;另一方面使每个粒子利用更多其他粒子的有用信息,粒子之间的竞争被增强.两个方面的结合将平衡速度和精度之间的矛盾.实验表明,该方法可以提高几何约束求解的效率和收敛性.     

多业务CDMA网络参数优化模型与算法

在考虑覆盖率、软切换率、业务信道拥塞率和功率损耗等网络质量指标的基础上,建立多业务CDMA网络参数优化问题的数学模型.通过分析模型的特点,设计了一种基于约束优化遗传算法(COGA)的求解方法,并给出了算法实现的各种关键技术.对一个实际算例进行实验研究,仿真结果表明算法能够有效地配置各种网络参数,网络性能得到提升,优于实际DT(driver test)的优化效果,从而表明所建模型和算法能够为多业务CDMA网络参数优化问题提供快速的解决方案,有效地指导实际的网络管理工作.     

求解含非线性参数的QoS路由的启发式算法

具有非线性参数的QoS路由分为含有非线性约束条件的QoS路由和含有非线性优化目标的QoS路由两类，它们都是NP问题．提出了两种启发式算法求解这两类QOS路由优化问题问题．对第一类问题，求解去掉非线性约束条件后的优化问题．如果找到的解满足非线性约束条件，则该解是最优解；否则在优化问题中添加一个新的线性约束，将已得到的解去掉，反复下去就可得到最终解．对第二类问题，将非线性优化目标换为约束条件中的线性参数，求解此优化模型，如果有解，则记录此时对应的非线性目标值．而后增加一个新的线性约束，去掉刚才得到的解，比较两次得到的非线性目标值，保留最小值．如果得到的解不满足该线性参数的约束条件，则算法结束；否则继续迭代．证明了两种算法的收敛性，并且时间复杂性为近似多项式时间．计算实例表明了算法的有效性．     

On Non-Uniform Rational B-Splines Surface Neural Networks

This article presents a simulation study for validation of an adaptation methodology for learning weights of a Hopfield neural network configured as a static optimizer. The quadratic Liapunov function associated with the Hopfield network dynamics is leveraged to map the set of constraints associated with a static optimization problem. This approach leads to a set of constraint-specific penalty or weighting coefficients whose values need to be defined. The methodology leverages a learning-based approach to define values of constraint weighting coefficients through adaptation. These values are in turn used to compute values of network weights, effectively eliminating the guesswork in defining weight values for a given static optimization problem, which has been a long-standing challenge in artificial neural networks. The simulation study is performed using the Traveling Salesman problem from the domain of combinatorial optimization. Simulation results indicate that the adaptation procedure is able to guide the Hopfield network towards solutions of the problem starting with random values for weights and constraint weighting coefficients. At the conclusion of the adaptation phase, the Hopfield network acquires weight values which readily position the network to search for local minimum solutions. The demonstrated successful application of the adaptation procedure eliminates the need to guess or predetermine the values for weights of the Hopfield network.