Passive filter design using genetic algorithms

The objective of this paper is to propose a new approach for designing a passive LC filter of the full-bridge rectifier by using genetic algorithms (GAs). The performance of the cost-effective passive LC filter for a constant load depends on the appropriate inductor and capacitor selection. Several design methods are reviewed and a novel design methodology using GAs is proposed in this paper. By using the proposed GA program, designer can quickly find appropriate parameter values to meet the desired circuit performance. Experimental results show that an appropriate combination of the inductor and capacitor selected by the proposed GA program can meet the desired power quality requirement. Different cases of design examples are shown in this paper to verify the performance of the proposed design methodology.     

A novel design methodology for reconfigurable frequency selective surfaces using genetic algorithms

In this paper, a new reconfigurable frequency selective surface (RFSS) design concept is introduced. A grid of simple metallic patches interconnected by a matrix of switches is proposed as the unit cell of an RFSS. The switches are independently addressable and provide significant transmission and reflection flexibility over a large range of frequencies. This flexibility is exploited by optimizing the switch settings using a genetic algorithm to produce a desired frequency response. The versatility of the design technique is demonstrated by presenting several examples of genetically optimized RFSS. The first example to be considered is a linearly polarized FSS that can be reconfigured for either single-, dual-, or tri-band operation. An RFSS design is also introduced that can be optimized to have a frequency response that is polarization independent in one state (i.e., for one combination of switch settings) and polarization dependent in another state.     

Optical orthogonal code design using genetic algorithms

A construction technique for optical orthogonal codes (OOCs) using genetic algorithms (GAs) is proposed. The performance of the GA OOCs is compared to that of four existing OOCs. Results show that the GA OOCs have a lower probability of error     

Distributed trigger counting algorithms for arbitrary network topology

The distributed trigger counting (DTC) problem is a fundamental block for many distributed applications. Particularly, such a problem is to raise an alert while the number of triggers received by the whole system reaches a pre‐defined amount. There have been a few algorithms proposed to solve the DTC problem in the literature. However, these existing algorithms are all under the assumption that each process knows what kind of network topology the whole system forms as well as playing distinct kind of role in the system. The foregoing assumption is not practical for wireless sensor networks because the network topology of a wireless sensor network cannot be obtained in advance, and the roles of all processes are basically identical during the computation. In this paper, we propose a novel distributed algorithm to solve the DTC problem, free of any aforementioned global assumption. Moreover, in order to reduce the message complexity of our algorithm, we further propose a more message‐efficient version, only with one extra requirement that all processes have learned ahead the number of processes in the system. Copyright © 2016 John Wiley & Sons, Ltd.     

Topological design of local-area networks using genetic algorithms

In this paper, we describe an algorithm for designing local-area networks (LANs) with the objective of minimizing the average network delay. The topology design includes issues such as determination of the number of segments in the network, allocating the users to the different segments, and determining the interconnections and routing among the segments. The determination of the optimal LAN topology is a very complicated combinatorial optimization problem. Therefore, a heuristic algorithm that is based on genetic ideas is used. Numerical examples are provided and the quality of the designs obtained by using the algorithm is compared with lower bounds on the average network delay that are developed     

E面分支波导耦合器的遗传算法优化设计

介绍了E面分支波导耦合器基于遗传算法的优化设计方法。详细讨论了遗传算法的优化策略和一个四端口5dB五分支波导耦合器设计实例，及该实例的等效网络综合结果和优化策略和一个四端口5dB五分支波导耦合器设计实例，及该实例的等效网络综合结果和优化后的HFSS软件仿真结果。通过比较可以发现，遗传算法优化结果完全满足期望的优化目标，经过优化设计，耦合器的频响特性有了明显改善。     

Solving large-scale SONET network design problems using bee-inspired algorithms

In the past years, the number of users of Internet-based applications has exponentially increased and consequently the request for transmission capacity or bandwidth has significantly augmented. When managed properly, the ring networks are uniquely suited to deliver a large amount of bandwidth in a reliable and inexpensive way. In this paper, we consider two problems that arise in the design of optical telecommunication networks, namely the SONET Ring Assignment Problem (SRAP) and the Intraring Synchronous Optical Network Design Problem (IDP), known to be NP-hard. In SRAP, the objective is to minimise the number of rings (i.e., DXCs). In IDP, the objective is to minimise the number of ADMs. Both problems are subject to a ring capacity constraint. To solve these problems, we propose two bee-inspired algorithms: Hybrid Artificial Bee Colony and Hybrid Bees Algorithm. We hybridise the basic form of these algorithms with local search, in order to refine newly constructed solutions. We also perform comparisons with other algorithms from the literature and use larger instances. The simulation results verify the effectiveness and robustness of the proposed algorithms.     

可重构网络中基于中心度与拓扑势排序的资源分配算法

可重构网络中的一个研究难点是虚拟资源的分配问题。首次将社会网络中心度和拓扑势研究引入到可重构网络虚拟资源分配问题中,在映射虚拟网络时考虑节点在网络中的位置重要性,提出了基于中心度和拓扑势的虚拟网映射算法。实验表明,所提出的新算法在提高资源分配效率,降低物理网络开销方面,与本领域以往算法相比,具有显著的进步。     

Implementation of a decoupled optimization technique for design ofswitching regulators using genetic algorithms

This paper presents an implementation of a decoupled optimization technique for design of switching regulators using genetic algorithms (GAs). The optimization process entails the selection of component values in a switching regulator, in order to meet the static and dynamic requirements. Although the proposed method inherits characteristics of evolutionary computations that involve randomness, recombination, and survival of the fittest, it does not perform a whole-circuit optimization. Thus, intensive computations, that are usually found in stochastic optimization techniques can be avoided. Similar to many design approaches for power electronics circuits, a regulator is decoupled into two components, namely the power conversion stage (PCS) and the feedback network (FN). The PCS is optimized with the required static characteristics, whilst the FN is optimized with the required static and dynamic behaviors of the whole system. Systematic optimization procedures are described and the technique is illustrated with the design of a buck regulator with overcurrent protection. The predicted results are compared with the published results available in the literature and are verified with experimental measurements     

Broad-band fragmented aperture phased array element design using genetic algorithms

In this paper, a synthesis procedure to design thin broad-band fragmented aperture array elements is described. The arrays are assumed to be infinite periodic and the elements consist of a conducting pattern etched on a dielectric backed by a groundplane. A genetic algorithm (GA) is used to design the conducting pattern, relative permittivity, and thickness of the dielectric substrate with respect to array scan and bandwidth performance. The fitness function in the GA is evaluated using a finite-difference time-domain code with periodic boundary conditions. For a substrate thicker than about 0.1 /spl lambda//sub L/ (/spl lambda//sub L/= wavelength at the lowest frequency in the frequency band investigated), it was found that a bandwidth of at least one octave can be obtained for arrays scanned within 45/spl deg/ from broadside.     

Solving virtual topology reconfiguration problem on survivable WDM networks by using simulated annealing and genetic algorithms

In a Wavelength Division Multiplexing (WDM) network, the performance of the virtual topology (VT) designed for a pre-specified traffic pattern can be improved by performing virtual topology reconfiguration (VTR). Simultaneously, the provision of survivability of WDM networks is important, because the transmission of huge data should be protected when a fiber fails. Thus, the combination of survivability and reconfiguration is an important issue in WDM networks. In this paper, the Virtual Topology Reconfiguration Problem (VTRP) in survivable WDM networks with a reconfiguration constraint is studied. Given the physical topology, dedicated path-protection VT, and a new traffic demand matrix, the goal of VTRP is to reconfigure the current VT under the pre-specified reconfiguration constraint so that the objective value can be minimized. The object cost of VTRP is the average weighted propagation delay (AWPD). Because designing a polynomial time algorithm to find the optimal solution of VTRP is impractical, in this paper, a simulated annealing (SA) algorithm and a genetic algorithm (GA) are proposed to solve this problem. Experimental results of these algorithms are also given.

Robust design of absorbers using genetic algorithms and the finite element-boundary integral method

A new method for the genetic algorithm (GA) based design of broadband, high-performance electromagnetic absorbers is discussed. The method gives rise to novel absorber designs with a geometrical complexity greater than that of absorbers typically in use today. The finite element-boundary integral method is applied to efficiently analyze the scattering from complex geometries occupied by given lossy material, and genetic algorithms are adopted to optimize the geometry parameters to minimize the overall reflection coefficients. In addition, a method is proposed for accelerating the convergence of the GA. Numerical results for absorbers are presented for wide-angle incidence over a broad frequency range considering both polarizations, and demonstrate the new technique's power and robustness.     

A compact charge-based model to study the nanoscale undoped double gate MOSFETs for nanoelectronic circuit design using genetic algorithms

The analytical modeling of nanoscale devices is an important area of computer-aided design for fast and accurate nanoelectronic design and optimization.In the present paper,a new approach for modeling semiconductor devices,nanoscale double gate DG MOSFETs,by use of the gradual channel approximation(GC) approach and genetic algorithm optimization technique(GA) is presented.The proposed approach combines the universal optimization and fitting capability of GA and the cost-effective optimization concept of quantum correction,to achieve reliable,accurate and simple compact models for nanoelectronic circuit simulations.Our compact models give good predictions of the quantum capacitance,threshold voltage shift,quantum inversion charge density and drain current.These models have been verified with 2D self-consistent results from numerical calculations of the coupled Poisson-Schrodinger equations.The developed models can also be incorporated into nanoelectronic circuit simulators to study the nanoscale CMOS-based devices without impact on the computational time and data storage.     

基于SVG的图形化网络拓扑显示模块设计

网络拓扑的图形显示对实现网络管理系统的可视化和人机交互有着重要意义.将SVG(可伸缩矢量图形)引入网络拓扑管理模块,给出了基于SVG的图形化网络拓扑显示系统模型,并分别就数据解析、图形显示给出了具体模块设计和实现方案.该系统运用于网络管理系统后,可极大改善网管系统软件的用户界面,提高图形化网络拓扑的更新速度,便于集成其他网络管理功能模块.     

Wire-antenna designs using genetic algorithms

There is a large class of electromagnetic radiators designated as wire antennas. As a rule, an inductive process is used to design these antennas. Either an integral equation is formulated or a simulator is used that gives the current distributions on the wires of the antenna, from which the electromagnetic properties of the antenna can then be determined. Once the antenna properties are known, the parameters are optimized, using guides such as intuition, experience, simplified equations, or empirical studies. However, using an electromagnetics simulator in conjunction with a genetic algorithm (GA), it is possible to design an antenna using a completely deductive approach: the desired electromagnetic properties of the antenna are specified, and the wire configuration that most closely produces these results is then synthesized by the algorithm. In this paper, we describe four antennas designed using GAs. The first is a monopole, loaded with a modified folded dipole that was designed to radiate uniform power over the hemisphere at a frequency of 1.6 GHz. The second antenna consists of seven wires, the locations and lengths of which are determined by the GA alone, that radiates waves with right-hand-circular polarization at elevation angles above 10°, also at 1.6 GHz. The last two antennas are modified Yagis. One is designed for a broad frequency band and very low sidelobes at a center frequency of 235 MHz. The other is designed for high gain at a single frequency of 432 MHz. We have built and tested these antennas     

Thinned arrays using genetic algorithms

Large arrays are difficult to thin in order to obtain low sidelobes. Traditional statistical methods of aperiodic array synthesis fall far short of optimum configurations. Traditional optimization methods are not well suited for optimizing a large number of parameters or discrete parameters. This paper presents how to optimally thin an array using genetic algorithms. The genetic algorithm determines which elements are turned off in a periodic array to yield the lowest maximum relative sidelobe level. Simulation results for 200 element linear arrays and 200 element planar arrays are shown. The arrays are thinned to obtain sidelobe levels of less than -20 dB. The linear arrays are also optimized over both scan angle and bandwidth.<>     

Benders decomposition algorithms for the fixed-charge relay network design in telecommunications

In translucent networks, signal regenerators that operate based on optical/electrical/optical (O/E/O) conversion are employed to handle the problems of physical impairments and resulting signal quality issues in long-distance transmissions. Regenerators are placed at the relay nodes in a translucent network in such a way that the optical signals are not transmitted farther than a threshold distance without a 3R regeneration (reamplification-reshaping-retiming). In this study, we consider a fixed-charge relay network design (FCRND) problem whose applications are found mainly in long distance translucent optical telecommunication networks. From a modeling perspective, FCRND combines prominent features of the uncapacitated single-assignment hub location (location of relays points) and the fixed-charge network design problems (choice of transmission links in the network and routing of signals) while additionally considering distance-based coverage constraints. We devise Benders decomposition based solution methodologies in which the algorithmic performance is further enhanced by devising strengthened and disaggregated Benders cuts, surrogate constraints for the master problem, and an upper bound heuristic to both obtain and tighten optimality Benders cuts. We obtain solutions within 2.0 % of optimality in very reasonable times as we illustrate in our computational study.     

A family of computationally efficient algorithms for multichannel signal processing—A tutorial review

This paper is concerned with the solution of block linear systems arising in multichannel digital signal processing. First the general problem of block matrix inversion and linear system solution is considered and a corresponding algorithm, recursive in nature, is developed, together with a block form of triangularization theorem. Subsequently these general schemes are specialized to block Toeplitz structures yielding most existing efficient algorithms. To this respect, matrices related to multichannel Wiener filtering, AR and ARMA modelling as well as s+1 steps ahead prediction are examined and related algorithms are described. Finally block banded Toeplitz systems are considered and two new efficient algorithms are presented, one recursive in nature and the other FFT based. They constitute natural extensions of methods already available for the single channel case and their derivation is simple due to the unified approach introduced in this paper.     

一种用于移动Ad Hoc网络广播的新型拓扑

泛洪引起的多余重播问题在移动Ad Hoc网络广播中是一个研究的热点,而多余重播问题与网络的拓扑结构息息相关。采用目前的拓扑结构所产生的多余重播量较大。文中提出一种新型的拓扑结构,通过对比原有拓扑结构和新型拓扑结构的多余重播量,证明新型拓扑的可行性,并与现有的广播技术相结合,给出相应的广播技术方案,有效地改善了多余重播问题。