Multiobjective programming using uniform design and genetic algorithm

The notion of Pareto-optimality is one of the major approaches to multiobjective programming. While it is desirable to find more Pareto-optimal solutions, it is also desirable to find the ones scattered uniformly over the Pareto frontier in order to provide a variety of compromise solutions to the decision maker. We design a genetic algorithm for this purpose. We compose multiple fitness functions to guide the search, where each fitness function is equal to a weighted sum of the normalized objective functions and we apply an experimental design method called uniform design to select the weights. As a result, the search directions guided by these fitness functions are scattered uniformly toward the Pareto frontier in the objective space. With multiple fitness functions, we design a selection scheme to maintain a good and diverse population. In addition, we apply the uniform design to generate a good initial population and design a new crossover operator for searching the Pareto-optimal solutions. The numerical results demonstrate that the proposed algorithm can find the Pareto-optimal solutions scattered uniformly over the Pareto frontier.     

Multiobjective genetic algorithm approach for a dual-feed circularpolarised patch antenna design

A design procedure for a dual feed network is presented to produce a circular polarised matched antenna involving eight design parameters with associated constraints. Determination of such design parameters has been made possible by utilising a multiobjective genetic algorithm (MGA) approach. The conditions for circular polarisation and impedance matching were the objective functions employed in the MGA. The associated constraints were the lengths and characteristic impedance values of the feed network. The return loss and axial ratio for a 5.8 GHz antenna were investigated and good agreement was obtained between simulated and practical measurements     

改进的遗传算法在延误飞机进场排序中的应用

近年来,随着空中交通量的显著增加,空中交通容易拥挤。特别是在空中交通流量特别大的机场终端区,交通流量已趋于饱和,容易出现航班的延误,因此,机场终端区实时流量管理已变得越来越重要。为了优化终端区的延误飞机进场排序,以减少延误代价,文中通过将目标函数设定为总延误代价最小。针对传统先到先服务方法在终端区延误飞机排序管理中效率较低的问题,文中提出了一种改进的遗传算法,引入交叉掩码,改进了传统遗传算法的染色体编码方式,从而进一步降低了飞机队列总延误代价。仿真结果表明,与先到先服务算法相比,改进后的遗传算法将总延误代价降低了61.52%,优化效果明显。     

基于相位混合算法的衍射光学元件优化设计方法

在光束整形衍射光学元件的设计中,为提高目标衍射图样的重构精度,提出了一种基于相位混合的迭代算法。该算法采用复振幅每次迭代循环的初始相位与返回相位的加权和为驱动函数,并以每次循环开始和结束时的光强比较作为光束相位变换的判据。简单讨论了Gerchberg-Saxton（GS）算法的缺陷,并以高斯分布-均匀分布和高斯分布-环分布为例,对比了改进算法与GS算法的设计结果。计算机仿真结果表明,改进算法的极限收敛精度比GS算法高几个数量级,其能量利用率、顶部不均匀性等指标也均优于GS算法。该算法能获得重构精度较高的输出图样,对衍射光学元件的优化设计具体参考意义。     

改进的波束成型遗传算法对宽角度干扰的抑制

将遗传算法应用于一维线性天线阵的波束成型,研究了该算法对宽角度干扰的抑制性能。提出了一种改进的二进制编码方法,引入若干参数用以方便地控制了解的精度和搜索空间。仿真结果表明,该算法可以有效地抑制宽角度范围的强干扰信号,收敛速度快,抑制深度可达30dB。与已发表的文献比,该算法得到了更好的干扰抑制效果。     

基于改进遗传算法的云教学平台研究

云计算技术迅猛发展,云计算辅助教学平台应运而生,具有网络化的海量教学数据资源存储与计算功能和瘦客户端等显著优点,云辅助教学平台数据量和用户量巨大的特点决定了其作业类型的多样性和数据密集性,云辅助教学平台的设计重点在高效率的资源管理和调度。文中设计云计算辅助教学平台的体系结构,并对云平台作业调度的原有自适应遗传算法做出改进,以传统遗传算法做基础,综合数据公平和本地性选择遗传基因,相比较传统算法,在响应用户需求上更高效。仿真实验结果显示改进后算法更能体现公平性、并提高了效率,更适于云计算机环境。     

应用于贴片机贴装顺序优化的遗传算法的比较和改进

元件贴装顺序优化是决定贴片机生产效率的关键问题,传统的解决贴装顺序优化问题的方法有遗传算法,蚁群算法,SS(伞布搜索法)等。使用较多的还是遗传算法。遗传算法中包含选择算子、交叉算子、变异算子,且编程思想简单,但容易出现局部最优,过早收敛等情况。在此,通过对遗传算法在贴装顺序优化应用的结果比较找到一种更适合的遗传算法,使之拥有较快的收敛速度和全局优化性。     

基于C#运用遗传算法的排课系统

排课问题是典型的组合优化和不确定性调度问题,并且是NP完全问题.随着高校的发展,在教务管理系统中使用的排课模型也变得越来越复杂,针对遗传算法排课中存在的初始解生成不合理及一周多学时课程不好安排的问题,为了避免同一门课程在一周内的不合理上课情况.针对这种情况,给出了排课问题的数学模型,提出了基于C#运用遗传算法解决方案.结果表明,该算法能比较有效的解决排课问题.该方法易于学习和应用,且不必依赖特殊的实现模式.     

Multiplier-less FIR filter design using a genetic algorithm

A new synthesis technique is described for multiplier-less FIR digital filters consisting of a cascade of primitive linear phase sections. For medium-order filters the search space for an optimal cascade is typically of the order of 10³⁰ and this can be examined in a computation efficient way using the parallel-search capability of a genetic algorithm (GA). A particular form of GA based upon multilevel or structured chromosomes has been developed for the primitive cascade problem. Initial results suggest that, for the cost of increased filter delay, a typical 2:1 advantage can be achieved in both VLSI chip area and clock rate compared to filters designed using the usual equiripple method     

Frequency selective surface design based on genetic algorithm

An optimisation procedure based on the genetic algorithm is applied to the design of frequency selective surfaces (FSSs) fabricated by printed circuit technology. The inclusion of the shape of the mask in the set of parameters controlling the optimisation scheme enables the requisite frequency and polarisation performance to be obtained     

A systematic design formulation for Butler matrix applied FFT algorithm

A systematic general design formulation for a Butler matrix (Bmatrix) is described. TheBmatrix design problem discussed is used to determine phase shift location and value in a matrix, when the number of beam (elements of array)M = 2^{N}and the scattering matrix for the hybrid couplers are specified. The design formulation presented is based on the fact that aBmatrix design procedure and an FFT algorithm are equivalent in fundamental concepts. It is shown that theBmatrix design procedure can be systematically formulated by the FFT algorithm modifications, which preserves the topological properties of the original signal flow diagram. A simple design formula has been established by this formulation.     

Winograd's algorithm applied to number-theoretic transforms

We show how to perform a number-theoretic transform (n.t.t.) using an algorithm analogous to that of S. Winograd for computing the discrete Fourier transform (d.f.t.). Using this algorithm, the range of data lengths and word lengths is much larger than that available with conventional fast n.t.t.s.     

Multiobjective optimization design of Yagi-Uda antenna

An optimization method, such as the steepest gradient methods, could not easily obtain globally optimum solutions for devising antenna design parameters that allow the antenna to simultaneously improve multiple performances such as gain, sidelobe level, and input impedance. The genetic algorithm (GA) is suitable for empirically solving optimization problems and is effective in designing an antenna. In particular, this method can solve the multiobjective optimization problem using various Pareto-optimal solutions in an extremely efficient manner. In this paper, the Pareto GA, by which various Pareto-optimal solutions for each objective function (performance) can be obtained in a single trial of a numerical simulation and which enables the selection of parameters in accordance with the design requirement, is applied to the multiobjective optimization design of the Yagi-Uda antenna. The effectiveness of the Pareto GA was demonstrated by comparing the performances obtained by the Pareto GA with those of the previously reported values, which were obtained by the conventional GA, and with the values of the design benchmark reference.     

微量遗传算法在波导短匹配负载设计中的应用

介绍了微量遗传算法(MGA)，并将之与小生境(Niche)技术及洗牌(Shuffle)技术相结合，优化选择遗传算法(GA)的初始群体，明显地减少了内存需求及运算工作量。从它和基本遗传算法(SGA)的比较结果可以看出，在计算精度基本相当的情况下，其计算时间比SGA明显减少，计算效率大为提高。并应用MGA对波导短匹配负载进行了优化设计，结果表明效果良好。     

Integer coded Pareto genetic algorithm design of constrainedantenna arrays

Thinned antenna arrays and arrays constructed with digital phase shifters are designed using a Pareto genetic algorithm for low beamwidth and sidelobe level. To facilitate this process, the standard simple genetic algorithm is augmented by special genetic representations and operators. The technique is applied to the design of both one and two dimensional arrays     

基于混合遗传算法的m序列波形优化设计

现代雷达体制多采用大时宽带宽积的m-序列二相编码脉冲压缩波形,解决信号波形优化问题即使信号波形的脉压比在尽量少损失SNR和主瓣宽度的基础上达到极值.对于m-序列,初始寄存器的选取是关键.对于较长的码,传统的优化方法由于运算量过大造成组合爆炸或陷入局部极值而无法找到最优,传统遗传算法也由于初始种群数的规模运算量比较大,将梯度搜索和遗传算法相结合的混合遗传算法很好的解决了这个问题,通过优化m-序列二相码波形的仿真和性能分析验证了该算法的可行性和有效性.     

Genetic algorithm based inversion of neural networks applied to optimised design of UWB planar antennas

An inversion of artificial neural networks using a genetic algorithm is presented for a novel concept of optimisation applied to UWB planar antennas of bow-tie type with respect to specified values of antenna performance in the frequency range 3.1-10.6 GHz. This efficient concept is shown to achieve significant reduction in computing time for optimisation. The multidimensional inversion is characterised by a simple composite fitness or target function that includes antenna parameters as a function of signal frequency or/and angle dependence. Good impedance matching and gain performance is achieved over the whole frequency range by adequately modifying the radiating contour profile of the conventional triangular bow-tie antenna.     

基于遗传算法的大规模企业网拓扑设计新算法

企业网拓扑结构设计中的子网划分问题实际上是一个多目标非线性数学规划问题,选取布线距离、流量分配、流量均衡和传输时延作为子网划分的主要制约因素,建立了相应的数学模型并通过遗传算法求解待优化的目标函数以完成子网划分。分析结果表明,此方法能较好地解决大规模网络拓扑设计中的子网划分问题。     

Loop-shaping techniques applied to the least-mean-squares algorithm

The least-mean-squares (LMS) algorithm is analysed as a feedback control system. It is shown that despite the fact that LMS is a time-variant system that in fact it behaves much as a linear time-invariant (LTI) closed-loop control system. Therefore, it is possible to treat the LMS algorithm as a control system in the classical sense and define properties such as bandwidth to determine how fast a response (and hence convergence) is maximally possible. Similarly, the steady-state error response to a deterministic noise-free input can also be calculated. Moreover, it is then shown that classical control-based loop-shaping techniques can be used to improve the performance of the algorithm.