一种基于遗传Hopfield神经网络求解TSP问题的算法

针对Hopfield网络求解TSP问题时出现无效解和收敛性能差的问题,对约束条件能量函数进行改进,构造了一种求解TSP问题的遗传Hopfield神经网络算法,并与经典Hopfield神经网络求解TSP方法进行对比.实验结果表明,本文算法具有更好的整体求解性能.     

A hill-jump algorithm of Hopfield neural network for shortest path problem in communication network

In this paper, we present a hill-jump algorithm of the Hopfield neural network for the shortest path problem in communication networks, where the goal is to find the shortest path from a starting node to an ending node. The method is intended to provide a near-optimum parallel algorithm for solving the shortest path problem. To do this, first the method uses the Hopfield neural network to get a path. Because the neural network always falls into a local minimum, the found path is usually not a shortest path. To search the shortest path, the method then helps the neural network jump from local minima of energy function by using another neural network built from a part of energy function of the problem. The method is tested through simulating some randomly generated communication networks, with the simulation results showing that the solution found by the proposed method is superior to that of the best existing neural network based algorithm.     

基于自适应进化神经网络算法的入侵检测

针对目前多数入侵检测系统的低检测率问题,提出一种自适应进化神经网络算法AENNA。基于遗传算法和BP神经网络算法,利用模拟退火算法的概率突跳和局部搜索强的特性对遗传算法进行改进,采用双种群策略的遗传进化规则实现BP神经网络权值和结构的双重优化;通过对遗传算法的交叉算子与变异算子的改进,设计一种自适应的神经网络训练方法。实验结果表明,基于AENNA的入侵检测方法能够有效提高系统的检测率并降低误报率。     

随机神经网络发展现状综述

随机神经网络 (RNN)在人工神经网络中是一类比较独特、出现较晚的神经网络 ,它的网络结构、学习算法、状态更新规则以及应用等方面都因此具有自身的特点 .作为仿生神经元数学模型 ,随机神经网络在联想记忆、图像处理、组合优化问题上都显示出较强的优势 .在阐述随机神经网络发展现状、网络特性以及广泛应用的同时 ,专门将RNN分别与Hopfield网络、模拟退火算法和Boltzmann机在组合优化问题上的应用进行了分析对比 ,指出RNN是解决旅行商 (TSP)等问题的有效途径     

函数优化问题的一种异步并行模拟退火算法

针对工程中的多极值点复杂函数的优化问题,提出一种完全异步的粗粒度并行模拟退火算法.在一定时间内将一条Markov链分裂成多条Markov链,并结合其他多种改进方法,获得了可扩展的并行效果,提高了算法应用的灵活性.数值计算表明,该方法可显著提高算法的收敛速度.     

一种基于免疫和Hopfield神经网络的多峰值优化算法

分析了免疫算法和Hopfield神经网络的优缺点,提出了一种解决多峰值函数优化问题的混合算法。Hopfield神经网络易于硬件实现,具有简单、快速的优点,但是对初始值具有依赖性以及容易陷入局部极值。免疫算法具有识别多样性的特点,但搜索效率和精度不高。将两算法结合起来,优势互补。首先用免疫算法寻优,然后对所得具有全局多样性的解进行聚类分析,所得聚类中心作为Hopfield神经网络的初始搜索点,最后利用Hopfield神经网络逐个寻优。实验表明,该算法是一种有效的求解多峰函数优化问题的方法,与免疫算法相比,搜索效率和精度都较高。     

利用遗传模拟退火算法优化神经网络结构

常用的神经网络是通过固定的网络结构得到最优权值,使网络的实用性受到影响。引入了一种基于方向的交叉算子和变异算子,同时把模拟退火算法引入了遗传算法,结合遗传算法和模拟退火算法的优点,提出了一种优化神经网络结构的遗传——模拟退火混合算法,实现了网络结构和权值的同时优化。仿真实验表明,与遗传算法和模拟退火算法相比,该算法优化的神经网络收敛速度较快、预测精度较高,提高了网络的处理能力。     

基于改进能量函数的混沌神经网络JSP解法

针对传统Hopfield神经网络(HNN)在求NP类问题的解时易陷入局部最优点的不足,提出基于改进能量函数的模拟退火混沌神经网络算法。通过在Hopfield神经网络中引入混沌机制,并结合退火策略控制混沌动态,有效避免了陷入局部极小的缺陷,因此将其用于求解JSP（作业车间调度）。算法改进了表示JSP的换位矩阵,给出了包含目标函数的能量函数,保证了网络的稳态输出为全局可行解。     

多约束选址—路径问题的改进混合遗传算法研究

选址—路径问题(LRP)同时解决设施选址和车辆路径问题,使物流系统总成本达到最小,在集成化物流配送网络规划中具有重要意义。针对带仓库容量约束和路径容量约束的选址—路径(CLRP)问题,提出了一种结合模拟退火算法的混合遗传算法进行整体求解。改进混合遗传算法分别对初始种群生成方式、遗传操作和重组策略进行改进,并实现了模拟退火的良好局部搜索能力与遗传算法的全局搜索能力的有效结合。运用一组Barreto Benchmark算例进行数值实验测试其性能,并将求解结果与国外文献中的启发式算法进行比较,验证了改进混合算法的有效性和可行性。     

A hybrid approach based on stochastic competitive Hopfield neural network and efficient genetic algorithm for frequency assignment problem

This paper presents a hybrid efficient genetic algorithm (EGA) for the stochastic competitive Hopfield (SCH) neural network, which is named SCH–EGA. This approach aims to tackle the frequency assignment problem (FAP). The objective of the FAP in satellite communication system is to minimize the co-channel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate increasing demands. Our hybrid algorithm involves a stochastic competitive Hopfield neural network (SCHNN) which manages the problem constraints, when a genetic algorithm searches for high quality solutions with the minimum possible cost. Our hybrid algorithm, reflecting a special type of algorithm hybrid thought, owns good adaptability which cannot only deal with the FAP, but also cope with other problems including the clustering, classification, and the maximum clique problem, etc. In this paper, we first propose five optimal strategies to build an efficient genetic algorithm. Then we explore three hybridizations between SCHNN and EGA to discover the best hybrid algorithm. We believe that the comparison can also be helpful for hybridizations between neural networks and other evolutionary algorithms such as the particle swarm optimization algorithm, the artificial bee colony algorithm, etc. In the experiments, our hybrid algorithm obtains better or comparable performance than other algorithms on 5 benchmark problems and 12 large problems randomly generated. Finally, we show that our hybrid algorithm can obtain good results with a small size population.     

数据缺失下学习贝叶斯网络的E-GSA算法

针对数据缺失条件下构建贝叶斯网络难度大的问题,研究了贝叶斯结构学习算法,提出了将条件独立性检验和评分-搜索相结合的算法.采用改进的混合算法对训练数据初始化,建立相应的初始网络,对已经拟合了训练数据信息的初始网络用遗传模拟退火算法进行训练以找到最佳的网络结构.给出了算法实施的具体步骤且通过实验验证了算法性能,并将实验结果与其他典型的算法进行比较,表明了算法具有更优的学习效果.     

一种新的传感器网络混合广播调度方法

由于传感器网络所使用无线信道的共享性和相互干扰, 节点间数据广播会产生资源冲突, 广播调度要解决的即是为每个节点分配到一个无冲突传输时隙, 其目标是找到最优时分复用(TDMA: time division multiple access)调度解, 使得帧长度最短而信道利用率最大. 提出基于神经网络的两阶段混合广播调度算法. 在阶段一, 使用改进的顶点着色算法来获得调度所需最短时隙数目; 在阶段二, 使用模糊Hopfield网络将节点模糊聚类为M类, 同类 节点可以在同一时隙被调度, 不同类节点必须在不同时     

基于连续Hopfield网络求解TSP的新方法

当连续Hopfield网络及其能量函数同时具有自反馈或不具有自反馈时,称之为一致连续Hopfield网络．在分析了一致连续Hopfield网络能量稳定性的基础上,进一步研究了当网络有自反馈,而其能量函数无自反馈的情况下,网络能量变化的性质,分别给出了使能量函数上升、下降和不变的条件．利用这一理论,可以克服由于梯度下降法所导致的网络能量函数总是下降,从而使网络陷入局部极小值或不可行解的现象．最后在这个理论的基础上我们给出了一种新的求解TSP(traveling salesman problem)的方法,仿真研究表明此方法对于求解TSP问题是很有效的．     

多级多商品流物流网络设计的优化模型与组合模拟退火算法*

以最小化系统内的建设费用、库存费用和运输费用为优化目标,建立可描述一般性带能力约束的多级多商品流物流网络设计问题的优化模型,并根据物流网络设计问题的特点,基于模拟退火算法的思想,设计组合模拟退火算法对模型进行求解。算例的计算结果表明,该优化方法运算快捷,结果正确合理,能为此类物流网络设计问题提供科学的决策依据;通过分析发现,存储费用和运输费用都会影响物流网络系统的总费用,但运输费用对系统总费用的影响程度相对要大得多。     

A simulated annealing for multi-criteria network path problems

This paper investigates an oriented spanning tree (OST) based simulated annealing (SA) for solving the multi-criteria shortest path problem (MSPP) as well as the multi-criteria constrained shortest path problem (MCSPP), especially for those with nonlinear objectives. As a popular search algorithm, because of “search-from-a-point” searching mechanism, there have been only a few attempts in extending SA to multi-criteria optimization, particularly, for various MSPPs. In contrast with the existing evolutionary algorithms (EAs), by representing a path as an OST, the designed SA provides an entirely new searching mechanism in sense of “search from a paths set to another paths set” such that both of its local and global search capabilities are greatly improved. Because the possibility of existing a feasible path in a paths set is usually larger than that of one path being feasible, the designed SA has much predominance for solving MCSPPs. Some computational comparisons are discussed and the test results are compared with those obtained by a recent EA of which the representing approach and the ideas of evolution operators such as mutation and crossover are adopted in most of the existing EAs for the shortest path problems. The test results indicate that the new algorithm is available for both of MSPPs and MCSPPs.     

一种适用于移动对等网络的分簇算法

通过分簇算法减小网络振动效应,延长网络的寿命是移动对等网络的研究重点之一。在研究Kautz图及其特性的基础上,提出一种基于Kautz图的移动对等网络分簇算法。在算法中,定义地址空间树,使用Kautz串作为节点标识,并运用后根序和宽度优先算法遍历地址空间树等一系列技术生成簇。同时设计了相关机制管理和维护簇结构,保证结构一致性。理论证明和实验评估表明,该分簇算法能有效减小振动效应,延长网络寿命。     

基于文化混合优化算法的旅行商问题求解

为更好地求解TSP问题,将遗传算法与模拟退火算法结合并纳入文化算法体系,提出一种求解旅行商问题的文化混合优化算法。该算法空间可分为独立并行的两部分：种群空间和信度空间。种群空间按照遗传退火混合算法实现进化,并将进化中的较优个体提供给信度空间,信度空间提取并利用较优个体所包含的信息来引导种群进化。通过求解TSP标准测试问题,将文化混合优化算法所求得的最优路径与其他优化算法所求结果相比,算法偏差均可降低0.6%～13.01%,表明了文化混合优化算法求解TSP问题的有效性与优越性。     

A neural network based on the generalized Fischer-Burmeister function for nonlinear complementarity problems

In this paper, we consider a neural network model for solving the nonlinear complementarity problem (NCP). The neural network is derived from an equivalent unconstrained minimization reformulation of the NCP, which is based on the generalized Fischer-Burmeister function ?_p(a,b)=‖(a,b)‖_p-(a+b). We establish the existence and the convergence of the trajectory of the neural network, and study its Lyapunov stability, asymptotic stability as well as exponential stability. It was found that a larger p leads to a better convergence rate of the trajectory. Numerical simulations verify the obtained theoretical results.     

一种基于改进遗传算法的径向基小波神经网络

为了提高神经网络进行函数拟合的精度,首先在三层径向基神经网络基础上通过增加网络层次和改变激励函数提出了一种四层径向基小波神经网络,并采用遗传算法来确定初始网络参数;其次针对遗传算法中容易早熟的缺点,在遗传算法中引入动态平衡策略,根据适应度的变化来动态改变遗传算法中交叉和变异概率,从而增加算法全局探索和局部开发的平衡能力;最后通过对函数拟合试验并与其他方法相比较表明了算法的有效性。     

A shortest path routing algorithm using Hopfield neural network with an improved energy function

A shortest path routing algorithm using the Hopfield neural network with a modified Lyapunov function is proposed. The modified version of the Lyapunov energy function for an optimal routing problem is proposed for determining routing order for a source and multiple destinations. The proposed energy function mainly prevents the solution path from having loops and partitions. Experiments are performed on 3000 networks of up to 50 nodes with randomly selected link costs. The performance of the proposed algorithm is compared with several conventional algorithms including Ali and Kamoun's, Park and Choi's, and Ahn and Ramakrishna's algorithms in terms of the route optimality and convergence rate. The results show that the proposed algorithm outperforms conventional methods in all cases of experiments. The proposed algorithm particularly shows significant improvements on the route optimality and convergence rate over conventional algorithms when the size of the network approaches 50 nodes.