自适应策略在实数编码遗传算法中的应用研究

为解决简单遗传算法收敛速度慢以及局部收敛问题,在研究自适应策略的基础上,提出了一种基于实数编码,综合精英保留策略、2/4竞争选择策略和自适应策略的改进遗传算法.该改进算法将自适应策略及其在遗传算法中的应用方法做了改进,仿真实验证明,该算法可以提高收敛速度,有效实现全局最优化.     

实数编码遗传和单纯形混合算法在膜系优化设计中的应用

在传统遗传算法(GA)基础上,通过引入标准偏差函数构造了新的适应度函数,同时提出了一种自动降温的方法来控制退火选择策略中的温度.将这种改进的实数编码遗传算法(FGA)和单纯型算法(SA)有机结合起来,形成了新的膜系优化算法-实数编码遗传和单纯形混合算法,并编制了优化程序.实例表明该算法优化性能优越,既具有强大全局搜索能力,又能很好地实现局部搜索功能.用该算法实现了中心波长534 nm,带宽35 nm的可见光波段凹陷滤波器和高性能中性分束镜.     

基于多种群遗传算法的膜系优化设计

在膜系设计中采用多种群遗传算法,以希望提高计算的效率和结果的可靠性.利用该算法进行膜系优化设计,并与一般遗传算法比较,计算结果表明,多种群遗传算法能够显著提高膜系设计中计算的效率和可靠性,有利于膜系自动设计的进行.最后,利用该方法设计了一种紫外区短通滤光片,取得了良好的设计结果.     

自适应遗传算法在ADS-B优化布站中的应用

为提高民航安全水平、空域容量、运行效率和服务能力,提出基于自适应遗传算法的基站选址优化方法。总结广播式自动相关监视(ADS-B)基站组网的意义、原则和评价指标。仿真ADS-B天线辐射特性,计算基站安全间隔。建立基站选址优化的数学模型,给出自适应遗传算法的求解步骤,采用实数编码和最优保存策略,实现ADS-B基站优化布站。实验结果表明,该方法优于基本遗传算法,能有效地指导寻优,提高组网优化效率,为后期基站的组网提供参考。     

一种新型的自适应混沌遗传算法

针对标准二进制编码遗传算法的缺陷,提出一种基于实数编码技术的新型自适应混沌遗传算法用于求解优化问题.该算法利用信息熵理论产生较好的初始群体分布,并依据概率分布函数构造杂交算子,同时结合混沌动力学特性和人工神经网络理论,设计了一种自适应混沌变异算子,使算法能有效维持群体多样性,防止和克服进化过程中的"早熟"现象,算法操作简单、易于实现.最后通过对几个经典测试函数的数值实验,验证了该算法在提高解的精度和加快收敛速度方面都有显著改善,从而为解决函数优化问题提供了一种行之有效的新方法.     

基于蚁群算法的弹道参数辨识方法研究

通过传统的计算方法难以得到航空武器系统弹道参数,为此提出一种基于蚁群算法的航空武器气动参数辨识算法。该算法将蚁群算法和实数编码遗传算法相结合,以给定的弹道表数据为标准,采用蚁群算法计算航空武器气动参数分段函数的分段边界点马赫数值,在此基础上采用实数编码遗传算法计算分段函数的多项式系数。采用该算法进行某型炸弹阻力系数辨识计算,计算结果表明:该算法可行,且计算的阻力系数精度高。计算结果已成功应用于某型航电火控系统的设计中。     

改进遗传算法在物流中心选址优化中的应用

根据配送中心选址问题的特点和要求,在最低运输成本基础上,采用离散形式选址问题的数学模型;针对遗传算法采用常数交叉率的缺点,设计随遗传代数下降的新型自适应交叉率公式;针对局部极值问题采用了实数编码,设计了相应的选择、交叉及变异遗传操作,提高了收敛速度和优化精度.仿真结果表明,改进的遗传算法在配送中心选址问题上具有明显的优势.     

采用自适应归一化实数编码遗传算法优化鱼眼镜头系统

针对遗传算法(GA)优化超多参量光学系统时鲁棒性 较差的问题,在混入逃逸函数实数编码GA(MERCGA)的基础上,进一步结合参量归一化和自适 应变异概率的措施,提出了自适应归一化 实数编码GA(ANRCGA)。用ANRCGA对鱼眼镜头光学系统案例进行优化设计,并应用 评价函 数和Zemax光线追迹方法对MERCGA和ANRCGA的优化结果作比较。结果表明,应用本文的ANRCG A 比引自专利的参考设计及MERCGA优化得到光学系统的成像质量有明显提高,算法的鲁棒 性和计算效率也到了改善。     

基于实数编码遗传算法的版画艺术设计方法研究

在艺术领域中,计算机图形处理技术的运用成为新的艺术创作手段和表现形式。为探讨遗传算法在艺术作品设计领域的应用,以便进行智能化的计算机辅助,提出通过实数编码遗传算法来实现版画艺术的数字化设计。首先,对版画的艺术设计过程和数码版画制作流程进行分析;然后,通过对版画图案的分析和概括,抽象出构成版画所需的纹样,利用实数编码遗传算法的交叉、变异操作生成丰富的纹样基因库;最后,以用户满意度为需求确定适应度函数并设计数码版画纹样组装方案流程。算法仿真结果验证了提出方法的可行性和有效性。     

基于改进形式的遗传算法研究

为了克服遗传算法的一些缺点，从遗传算法初始群体的均匀生成、十进制整数与实数混合编码方式的采用、遗传算予的自适应生成、最优个体的无遗传保留等策略上改进了现有遗传算法，提出了一种改进形式的遗传算法。仿真实验表明，该改进遗传算法能大大提高遗传算法的收敛速度，有效防止“早熟”现象，明显改善了算法的计算稳定性。     

Optimal Design and Control of CPU Heat Sink Processes

This paper considers the optimal plate fin design and control for central processing unit (CPU) heat sink processes. First, we apply a finite element method to investigate the heat transfer phenomena of a heat sink process. To have a better heat dispersion performance, a real-coded genetic algorithm is then utilized to search for an optimal set of plate-fin shape parameters. The objective function to be minimized is the entropy generation rate which can take simultaneously the two major factors, heat transfer rate and air resistance, into consideration in the design. The present optimization scheme is able to achieve a better design for heat dispersion than existing methods. To attenuate environmental and time-varying disturbances, a direct adaptive control scheme is then developed for the CPU heat sink process. It is based on using a bounded single neuron controller (SNC) along with a parameter tuning algorithm to regulate the temperature of a selected control point. Extensive comparisons of the SNC-based control performance with the on-off control as well as a PI controller show that the proposed scheme provides excellent control performance despite the existence of unexpected process uncertainties.     

基于实数编码遗传算法的多波束天线优化设计

为了在服务区内获得良好的多波束性能,提出了一种新的单口径单馈源多波束天线设计方法。通过采用对反射面进行赋形的方法来解决传统单口径单馈源多波束中交叠增益与旁瓣电平之间的矛盾。在对反射面优化的过程中将反射面的形变量作为优化变量,然后利用实数编码遗传算法对其进行优化从而实现更高的增益和载干比(C/ I)性能。最终利用该算法对一个服务区为某区域的多波束天线进行了优化设计和分析,仿真结果说明了该优化方法的有效性。     

Electromagnetic inverse scattering of two-dimensional perfectlyconducting objects by real-coded genetic algorithm

Shape reconstruction of two-dimensional perfectly conducting objects using noisy measured scattering data is considered. The contour of each conducting object is denoted by a shape function in the local polar coordinate which is approximated by a trigonometric series. A point-matching method is used to solve the scattering problem. The main idea of the inversion algorithm is to cast the inverse problem into a restrained minimization problem and to solve it by the real-coded genetic algorithm (RGA). The performance of this algorithm is demonstrated by numerically reconstructing arbitrarily shaped objects and by a detailed comparison with both the standard genetic algorithm (SGA) and the Newton-Kantorovitch method     

Polynomial approximated phase-only multiple sector beam patterns of linear antenna arrays with pre-fixed amplitude distribution using real-valued genetic algorithms

In this paper, the authors present second degree polynomial approximated phase-only synthesis of two different types of symmetric sector or flat-top beams of equally spaced linear antenna arrays of isotropic radiators with single pre-fixed cosine on a pedestal amplitude distributions using real-coded or real-valued genetic algorithm. This gives a regular shape to the phase distributions of the radiators. Regular shape phase distributions result in simpler feed network design compared to irregular shape phase distributions. A good agreement between desired and synthesized pattern using genetic algorithm (GA) is reported.     

An Improved Real-Coded Genetic Algorithm and Its Application

Real-coded genetic algorithm（RGA）usually meets the demand of consecutive space problem.However,compared with simple genetic algorithm（SGA）RGA also has the inherent disadvantages such as prematurity and slow convergence when the solution is close to the optimum solution.This paper presents an improved real-coded genetic algorithm to increase the computation efficiency and avoid prematurity,especially in the optimization of multi-modal function.In this method,mutation operation and crossover operation are improved.Examples are given to demonstrate its com p utation efficiency and robustness.     

基于自适应遗传算法多项式预失真技术研究

提出了一种基于自适应遗传算法的多项式预失真补偿方法,该方法的基本原理是利用模拟进化类算法对目标函数进行逼近,对待求解参数进行辨识,进而得到多项式预失真模型。该算法具有良好的收敛性、全局性和鲁棒性,且具有较高的计算效率,为解决数字预失真自适应算法提供了一个新的参考。同时将自适应遗传算法应用于功放线性化设计中并进行了仿真。仿真分析表明,采用模拟进化类的遗传算法对功放失真抑制效果明显。     

Robust vector quantization for wireless channels

This study focuses on two issues: parametric modeling of the channel and index assignment of codevectors, to design a vector quantizer that achieves high robustness against channel errors. We first formulate the design of a robust zero-redundancy vector quantizer as a combinatorial optimization problem leading to a genetic search for a minimum-distortion index assignment. The performance is further enhanced by the use of the Fritchman (1967) channel model that more closely characterizes the statistical dependencies between error sequences. This study also presents an index assignment algorithm based on the Fritchman model with parameter values estimated using a real-coded genetic algorithm. Simulation results indicate that the global explorative properties of genetic algorithms make them very effective in estimating Fritchman model parameters, and use of this model can match index assignment to expected channel conditions     

Automatic design of frequency sampling filters by hybrid geneticalgorithm techniques

A new method of designing recursive and nonrecursive frequency sampling filters is presented. We investigate the use of a hybrid real-coded genetic algorithm (GA) for optimising transition sample values to give the maximum stopband attenuation. A modification allows the coefficient wordlength to be optimized concurrently, thereby reducing the overall number of design steps and simplifying the design process. The technique is able to consistently optimize filters with up to six transition samples. Designing digital filters is a complex process involving optimization at several discrete design steps. The techniques presented could form the basis for integrating several of the optimizations. Investigations into increasing this integration by using a binary-coded GA to optimize nonlinear phase, quantized coefficient FIR filters are introduced, with an analysis of the difficulty of the problem from a GA perspective     

一种改进的一维模糊熵图像分割算法

传统的一维最大模糊熵图像分割算法对于图像的局部信息干扰噪声处理能力存在不足。文中研究实数编码混沌量子遗传算法（RCQGA）与一维模糊熵算法相结合的新算法。该算法将图像的空间信息和像素信息引入到一维模糊熵图像分割算法中,并运用实数编码混沌量子遗传算法对一维最大模糊熵图像分割算法进行改进,从而提高了一维最大模糊熵分割精度。研究结果表明,该算法分割效果明显优于传统一维模糊熵图像分割算法,并具有较强的抗噪性能。