模拟退火算法在无线传感器网络定位中的应用

近年来,随着对无线传感器网络研究的不断深入,节点定位问题受到了国内外研究者的极大关注。在深入研究无线传感器网络节点定位和模拟退火算法的基础上,提出了一种新颖的无线传感器网络节点定位算法,着重介绍了算法的基本原理和实现方法。仿真实验结果表明,该算法取得了良好的定位效果。该算法设计简单,计算量小,比较适合于无线传感器网络的节点定位。     

Telecommunication access network design with reliability constraints

In this paper, we study the problem of the design of telecommunication access networks with reliability constraints. These networks form an important part of the telecommunications infrastructure of large organizations, such as banks. Using data patterned after an actual bank network in the U.S., we formulate an optimization model for this problem which specifically takes into account the various cost, and discount structures offered by telecommunication carriers. We then develop dedicated solution procedures for obtaining solutions. Starting from a cluster solution, we then use perturbation techniques which we developed specifically for this problem within an overall simulated annealing solution algorithm. We show how to make the solution procedure more efficient by implicitly determining the values for many variables. We then report the results of our computational testing for a variety of problems. We compare our solution to a lower bound obtained using a linear programming relaxation. We show that substantial cost savings can be realized with our model, and solution procedure. Finally, we discuss which types of annealing steps in the simulated annealing algorithm are important.     

Genetic simulated annealing‐based coverage‐enhancing algorithm for multimedia directional sensor networks

Multimedia directional sensor network is one kind of directional sensing systems, whose coverage scheme is quite different from the omnidirectional sensing system. And it is often used in atrocious environmental surveillance, such as nuclear contaminative areas, where people can hardly arrive. In this paper, a genetic simulated annealing‐based coverage‐enhancing algorithm (GSACEA) is proposed as a coverage‐enhancing method in multimedia directional sensor networks. Firstly, GSACEA combines the genetic algorithm and simulated annealing algorithm into an algorithm with new architecture. Then, the proposed GSACEA is applied for the purpose of coverage‐enhancing in the case of directional sensor networks with rotational direction‐adjustable model. Finally, after series actions of genetic simulated annealing, the proposed method can find the approximate solution to the best area coverage rate. And according to the results of simulations, which compared the proposed method with several other classic coverage‐enhancing methods in directional sensor networks, it could be concluded that GSACEA can achieve the highest area coverage rate of directional sensor networks and reduce the iterative computing times simultaneously. Copyright © 2014 John Wiley & Sons, Ltd.     

一种改进的RBF整定PID及其仿真实现

文中在分析RBF神经网络整定PID算法优缺点的基础上,给出了一种采用遗传模拟退火算法来优化网络结构和权值参数的RBF神经网络,将改进的RBF神经网络用于整定PID控制,并给出了相应的仿真测试例子。仿真实验结果表明,与采用梯度法优化网络权值等参数的RBF神经网络相比,给出的优化算法能更好地辨识控制系统,具有通用性好、调节精度高、在抑制超调量能力强等优点。     

一种启发式的计算机局域网拓扑优化设计方法

研究了计算机局域网优化设计问题的数学模型。采用稀疏哈夫曼树来描述网络拓扑结构,并将遗传算法和模拟退火法相结合,提出了一种启发式搜索算法,实现了以最小平均时延为目标的局域网拓扑优化设计。实验结果表明启发式算法具有较好的性能。     

Meta‐heuristic algorithms for channel scheduling problem in wireless sensor networks

The channel scheduling problem is to decide how to commit channels for transmitting data between nodes in wireless networks. This problem is one of the most important problems in wireless sensor networks. In this problem, we aim to obtain a near‐optimal solution with the minimal energy consumption within a reasonable time. As the number of nodes increases in the network, however, the amount of calculation for finding the solution would be too high. It can be difficult to obtain an optimal solution in a reasonable execution time because this problem is NP‐hard. Therefore, most of the recent studies for such problems seem to focus on heuristic algorithms. In this paper, we propose efficient channel scheduling algorithms to obtain a near‐optimal solution on the basis of three meta‐heuristic algorithms; the genetic algorithm, the Tabu search, and the simulated annealing. In order to make a search more efficient, we propose some neighborhood generating methods for the proposed algorithms. We evaluate the performance of the proposed algorithms through some experiments in terms of energy consumption and algorithm execution time. The experimental results show that the proposed algorithms are efficient for solving the channel scheduling problem in wireless sensor networks. Copyright © 2011 John Wiley & Sons, Ltd.     

一种基于计算智能的组播路由算法

在通信网络中,多约束组播通信是提高网络运行效率和服务质量的重要途径。一些启发式的算法已经被用来解决多约束条件下的组播路由问题,如模拟退火算法,遗传算法,蚁群算法和粒子群优化算法等。然而,这些算法在求解多约束组播路由问题时存在收敛速度低和计算复杂度高的问题。萤火虫群优化(GSO)算法是一种近期在计算智能领域出现的卓越算法,它可以在一定程度上解决多约束组播树生成过程中收敛速度低和计算复杂度高的问题。提出了一种基于GSO的多约束组播树生成算法(GSO-MCM)。该算法可有效生成满足多约束要求的组播路由树。仿真结果表明提出的GSO-MCM算法在求解和收敛速度,以及网络规模适应性方面均有良好的性能。     

改进优化算法FBG传感网复用能力的研究

针对布拉格光栅在构成传感网络时会出现光谱重叠的问题,通过对模拟退火算法中退火因子的修改,以及在退火过程中给予合理的回温操作,改进了算法的计算效率和收敛性,并将改良后的退火算法与粒子群算法结合,可对光谱重叠中各个光栅的波长进行识别。通过对单个及多个光谱重叠的识别结果分析,验证了本算法对光谱重叠的区分具有可行性。实验结果表明:当三个光栅发生两个光谱重叠现象时,改进模拟退火的粒子群算法能够对其光栅进行识别和区分,并且能够将波长的辨识误差控制在±5 pm左右。     

A novel heuristic simulation‐optimization method for critical infrastructure in smart transportation systems

A simulation‐based optimization is a decision‐making tool that helps in identifying an optimal solution or a design for a system. An optimal solution and design are more meaningful if they enhance a smart system with sensing, computing, and monitoring capabilities with improved efficiency. In situations where testing the physical prototype is difficult, a computer‐based simulation and its optimization processes are helpful in providing low‐cost, speedy and lesser time‐ and resource‐consuming solutions. In this work, a comparative analysis of the proposed heuristic simulation‐optimization method for improving quality‐of‐service (QoS) is performed with generalized integrated optimization (a simulation approach based on genetic algorithms with evolutionary simulated annealing strategies having simplex search). In the proposed approach, feature‐based local (group) and global (network) formation processes are integrated with Internet of Things (IoT) based solutions for finding the optimum performance. Further, the simulated annealing method is applied for finding local and global optimum values supporting minimum traffic conditions. A small‐scale network of 50 to 100 nodes shows that genetic simulation optimization with multicriteria and multidimensional features performs better as compared to other simulation‐optimization approaches. Further, a minimum of 3.4% and a maximum of 16.2% improvement is observed in faster route identification for small‐scale IoT networks with simulation‐optimization constraints integrated model as compared to the traditional method. The proposed approach improves the critical infrastructure monitoring performance as compared to the generalized simulation‐optimization process in complex transportation scenarios with heavy traffic conditions. The communicational and computational‐cost complexities are least for the proposed approach.     

Nature-inspired optimization of Moving Access Point-based radio networks

Wireless communication systems have penetrated into almost all parts of human life. They have become essential and pervasive and do affect both personal and professional aspects of our lives. While most types of wireless networks, such as cellular mobile communication networks, have been exhaustively studied and optimized, in order to handle various situations, there are also contexts which require efficient handling, e.g., moving hotspots, areas that have lost their infrastructure, and areas with hard morphology. The motivation for this work is the efficient handling of these important contexts by the use of Moving Access Points (MAPs). MAPs are capable of autonomously moving and establishing a radio network in short time, with limited centralized management. The radio network provides wireless access to users, is based on ad hoc connectivity (self-adapting mesh network concept), and has some elements acting as gateways to a wide-area infrastructure. The main purpose of this paper is to find the optimal position of the MAPs, i.e., the ones which require minimum movement and telecommunication cost. In order to achieve this goal, an innovative algorithm that combines the well-known simulated annealing algorithm with the characteristic of pheromone of the ant colony optimization is proposed. This is done in order to exploit ant colony optimization concepts in the related problem of the optimization of the MAP-based radio network design. The operation of the algorithm is validated by being applied to three most indicative scenarios, in which it is compared to the pure simulated annealing and a brute-force method, respectively. Our scheme exhibits a 5% improved performance in terms of solution quality in medium and large size problems, a 9% improved speed for the estimation of the suboptimal solution and 15% quicker adaptation to new context, without impacting the computational complexity.     

二值纹理图象重建的平均场技术

根据平均场神经网络，本文提出了一种二值纹理图象重建算法，纹理图象用马尔可夫随机场模型描述。重建算法是一个自适应过程，递归地进行模型参数估计和图象重建。平均场方法将图象搜索中基于模拟降温的统计松驰方程转化为一组确定性方程，从而有效地提高了计算效率。计算机实验结果证实了重建算法的有效性。     

一种新的应用于并行数字仿真的电路划分算法

为减少仿真的时间,并行仿真技术广泛应用于大规模集成电路的计算.研究发现为并行仿真所做的电路划分对提高仿真速度有相当重要的作用.该文提出一个新的多层启发式的拓扑电路划分算法,展示了将该算法应用于一个实际的电路并行仿真系统(Discovery)所获得的仿真加速.该算法主要着眼于平衡计算负载,减少整个仿真网络通信量这两方面来提高仿真系统的性能.试验结果表明该算法比其他的电路划分算法取得了更好的性能提高.     

Simulated Annealing Based Multi-constrained QoS Routing in Mobile ad hoc Networks

Multi-constrained quality-of-service routing (QoSR) is to find a feasible path that satisfies multiple constraints simultaneously, which is a big challenge for mobile ad hoc networks (MANETs) where the topology may change constantly. It has been proved that such a problem is NP-complete. Heuristic algorithms with polynomial and pseudo-polynomial-time complexities are often used to deal with this problem. However, existing solutions, most of which suffered either from excessive computational complexities or from low performance were proposed only for wired networks and cannot be used directly in wireless MANETs. In this paper a novel QoS routing algorithm based on Simulated Annealing (SA_RA) is proposed. This algorithm first uses an energy function to translate multiple QoS weights into a single mixed metric and then seeks to find a feasible path by simulated annealing. The paper outlines simulated annealing algorithm and analyzes the problems met when we apply it to QoSR in MANETs. Theoretical analysis and experiment results demonstrate that the proposed method is an effective approximation algorithms showing better performance than the other pertinent algorithm in seeking the (approximate) optimal configuration within a period of polynomial time.     

A stochastic multi-scale model of electrical function in normal and depleted ICC networks

Multi-scale modeling has become a productive strategy for quantifying interstitial cells of Cajal (ICC) network structure-function relationships, but the lack of large-scale ICC network imaging data currently limits modeling progress. The single normal equation simulation (SNESIM) algorithm was utilized to generate realistic virtual images of small real wild-type (WT) and 5-HT(2B)-receptor knockout (Htr2b(-/-)) mice ICC networks. Two metrics were developed to validate the performance of the algorithm: 1) network density, which is the proportion of ICC in the tissue; and 2) connectivity, which reflects the degree of connectivity of the ICC network. Following validation, the SNESIM algorithm was modified to allow variation in the degree of ICC network depletion. ICC networks from a range of depletion severities were generated, and the electrical activity over these networks was simulated. The virtual ICC networks generated by the original SNESIM algorithm were similar to that of their real counterparts. The electrical activity simulations showed that the maximum current density magnitude increased as the network density increased. In conclusion, the SNESIM algorithm is an effective tool for generating realistic virtual ICC networks. The modified SNESIM algorithm can be used with simulation techniques to quantify the physiological consequences of ICC network depletion at various physical scales.     

Statistical analysis of a two-layer backpropagation algorithm usedfor modeling nonlinear memoryless channels: the single neuron case

Neural networks have been used for modeling the nonlinear characteristics of memoryless nonlinear channels using backpropagation (BP) learning with experimental training data. In order to better understand this neural network application, this paper studies the transient and convergence properties of a simplified two-layer neural network that uses the BP algorithm and is trained with zero mean Gaussian data. The paper studies the effects of the neural net structure, weights, initial conditions, and algorithm step size on the mean square error (MSE) of the neural net approximation. The performance analysis is based on the derivation of recursions for the mean weight update that can be used to predict the weights and the MSE over time. Monte Carlo simulations display good to excellent agreement between the actual behavior and the predictions of the theoretical model     

Real-time traffic management by a parallel algorithm

The joint access-control and routing problem for distributed networks is formulated as an equilibrium programming problem (EPP). The strategy, for an upcoming control period, decreases call blocking and balances network load by maximizing network residual capacity, and fairly rejects at the source the source-destination demands that are expected to exceed network capacity. The EPP formulation allows for both decentralized implementation of the joint access control and routing problem and massive parallelization of the optimization procedure to satisfy real-time requirements. The computational complexity of the algorithm decreases proportionally with the number of processors used. The convergence of the decentralized algorithm to the network-wide optimum is proved. Compared to the case where subnet EPPs are solved independently by each controller, simulation results show significantly better utilization of network resources when subnet controllers jointly solve the network-wide EPP. Performance analysis of the algorithm and numerical studies show that the algorithm is well suited for real-time implementation in large networks or internetworks     

Efficient algorithms for scheduling multiple bulk data transfers in inter‐datacenter networks

Bulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter‐datacenter traffic. These bulk transfers consume massive bandwidth and further increase the operational cost of datacenters. The advent of store‐and‐forward transfer mode offers the opportunity for cloud provider companies to transfer bulk data by utilizing dynamic leftover bandwidth resources. In this paper, we study the multiple bulk data transfers scheduling problem in inter‐datacenter networks with dynamic link capacities. To improve the network utilization while guaranteeing fairness among requests, we employ the max–min fairness and aim at computing the lexicographically maximized solution. Leveraging the time‐expanded technique, the problem in dynamic networks is formulated as a static multi‐flow model. Then, we devise an optimal algorithm to solve it simultaneously from routing assignments and bandwidth allocation. To further reduce the computational cost, we propose to select an appropriate number of disjoint paths for each request. Extensive simulations are conducted on a real datacenter topology and prove that (i) benefiting from max–min fairness, the network utilization is significantly improved while honoring each individual performance; (ii) a small number of disjoint paths per request are sufficient to obtain the near optimal allocation within practical execution time. Copyright © 2013 John Wiley & Sons, Ltd.     

Hybrid switching and p-routing for optical burst switching networks

One promising switching technology for wavelength-division multiplexing optical networks is optical burst switching (OBS). However, there are major deficiencies of OBS. (1) The delay offset between a control message and its corresponding data burst is based on the diameter of a network. This affects network efficiency, quality-of-service, and network scalability.( 2) OBS adopts one-way resource reservation scheme, which causes frequent burst collision and, thus, burst loss. We address the above two important issues in OBS. In particular, we study how to improve the performance of delay and loss in OBS. To reduce the end-to-end delay, we propose a hybrid switching scheme. The hybrid switching is a combination of lightpath switching and OBS switching. A virtual topology design algorithm based on simulated annealing to minimize the longest shortest path through the virtual topology is presented. To minimize burst collision and loss, we propose a new routing algorithm, namely, p-routing, for OBS network. The p-routing is based on the wavelength available probability. A path that has higher available probability is less likely to drop bursts due to collision. The probability-based p-routing can reduce the volatility, randomness, and uncertainty of one-way resource reservation. Our studies show that hybrid switching and p-routing are complementary and both can dramatically improve the performance of OBS networks.     

Molecular Origins of the Mechanical Behavior of Hybrid Glasses

Hybrid organic‐inorganic glasses exhibit unique electro‐optical properties along with excellent thermal stability. Their inherently mechanically fragile nature, however, which derives from the oxide component of the hybrid glass network together with the presence of terminal groups that reduce network connectivity, remains a fundamental challenge for their integration in nanoscience and energy technologies. We report on a combined synthesis and computational strategy to elucidate the effect of molecular structure on mechanical properties of hybrid glass films. We first demonstrate the importance of rigidity percolation to elastic behavior. Secondly, using a novel application of graph theory, we reveal the complex 3‐D fracture path at the molecular scale and show that fracture energy in brittle hybrid glasses is fundamentally governed by the bond percolation properties of the network. The computational tools and scaling laws presented provide a robust predictive capability for guiding precursor selection and molecular network design of advanced hybrid organic‐inorganic materials.