自适应混合遗传算法在弹药装载中的应用研究

多约束条件下的弹药装载问题是一个复杂的组合优化问题,属于NP 完全问题,其求解是很困难的。本文在考虑弹药装载中各类约束条件的情况下,对简单遗传算法进行了多方面改进,提出了一种自适应混合遗传算法,来求解弹药装载问题。本文对该算法的编码和解码过程,以及复制算子、交叉算子和变异算子的构建,进行了详细的阐述,给出了使用该算法求解弹药装载问题的具体实现方法。     

基于改进遗传算法的试卷生成算法研究

针对应用传统遗传算法在组卷中出现的早熟和收敛速度慢等问题,提出基于改进遗传算法的试卷生成算法。详细介绍改进的遗传算法应用于组卷的步骤,包括编码方法、适应度函数、交叉算子和变异算子的确定等关键内容。该算法采用分组自然数形式进行个体编码,同时,一改传统交叉方法,采用自适应交叉概率和遗传概率的方法进行运算。仿真实验表明,该算法有效提高了组卷的效率。     

基于自适应免疫遗传算法的多序列比对

针对多序列比对问题提出一种基于自适应免疫遗传算法的多序列比对方法。为了保持群体中个体的多样性，以及加快算法的收敛速度，该算法中交叉概率和变异概率根据群体的多样性自适应调整，并且在选择免疫算子时采用择优策略，将免疫算子引入到自适应遗传算法中，通过对个体接种疫苗来进一步提升个体的存活能力。     

基于改进遗传算法的非均匀稀布阵列优化

提出了一种对非均匀稀布阵列阵元间距进行优化的改进的遗传算法。该算法在编码时,将阵元间距依据尺寸步长划分为不同的节点,通过对节点的二进制编码,灵活实现带约束阵元间距的遗传编码。在遗传操作过程中,依据不同的进化阶段自适应设定算子系数,有效地提高了遗传算法的收敛性。文中采用同时考虑副瓣电平和渡束宽度的双适应度函数,使优化得到的天线阵列方向图比采用单适应度函数有较好的综合性能。最后通过大量仿真结果验证了这一算法的有效性。     

一种高效的编码感知路由算法设计

针对现有的网络编码路由技术中存在的组合优化性能较差,如计算开销较大、数据交互复杂以及路由构建周期较长等问题,以遗传算法为理论基础,提出了一种改进的网络编码感知路由算法.该算法利用遗传算法的高效组合优化功能,重新构造了其网络编码感知路由的染色体表达、适应度函数以及遗传操作等,并添加了一种修复模块.实验结果表明,该算法与同类型的网络编码路由算法相比,其平均路由构造时间较短、网络吞吐量大,展现出较强的组合优化性能,并具有强优化的寻址能力.     

阵列天线波束赋形的混合遗传算法优化

本文提出了一种应用于阵列天线波束赋形优化的混合遗传算法.该算法将简化的步长加速法作为一个局部搜索算子,融入到基于实数编码的遗传算法中.局部搜索算子的引入,使得新算法同时具有了传统遗传算法在全局搜索时的鲁棒性和步长加速法在局部搜索中搜索较快的优点.文中分别利用该算法对16元直线阵列进行了平顶波束和余割平方波束赋形优化,仿真结果表明了该算法在阵列天线波束赋形优化问题上的应用是有效的.     

基于群体行为的自适应变异算子鱼群算法

针对人工鱼群算法的不足,考虑了包括鱼群个体之间的相互感知作用、群体的领导模式并结合萤火虫群中个体的光强吸引度在内的群体行为特点来对鱼群行为进行完善.同时,在算法改进方面,采用了自适应步长和视野,并且引入了Gauss变异算子和遗传算法在一定情况下对鱼群个体进行变异操作.在此基础上,提出了一种新型自适应变异算子的鱼群算法.通过典型函数验证结果表明该算法在收敛速度、精度、稳定性及克服早熟能力方面都有了显著的提高.     

自适应遗传视频运动对象分割算法

为了快速准确地分割视频运动对象,提出一种新的自适应遗传视频运动对象分割算法.该算法通过完善进化机制,引进自适应初代个体、自适应选择算子、自适应调整交叉率和变异率以及终止判决等,有效解决了遗传算法收敛速度幔和群体过早成熟的问题.实验结果表明,新算法不但缩短了分割时间,而且取得了良好的分割效果.     

基于复数GA的波束空间自适应调零处理

建立了直接辐射阵列多波束天线波束形成的数学模型.提出一种改进的复数编码遗传算法,在算法的初期和后期采用不同的配对方案,在奇数代和偶数代分别采用外推、内插和正交外推内插算子,克服了已有算法的早熟和对初始群体依赖等缺陷,提高了算法的全局收敛能力.采用记忆算子使算法能够对抗位置跳变干扰.模拟实验结果表明,该波束空间自适应调零新方法能够应用于LEO卫星抗干扰,且调零分辨率高、性能稳定可靠,可以获得比传统自适应波束形成算法更好的干扰抑制性能.     

实数遗传算法的改进及性能研究

提出一种粒子群优化方法(PSO)与实数编码遗传算法(GA)相结合的混合改进遗传算法(HIGAPSO).该方法采用混沌序列产生初始种群、非线性排序选择、多个交叉后代竞争择优和变异尺度自适应变化等改进遗传操作;并通过精英个体保留、粒子群优化及改进遗传算法(IGA)三种策略共同作用产生种群新个体,来克服常规算法中收敛速度慢、早熟及局部收敛等缺陷.通过四个高维典型函数测试结果表明该方法不但显著提高了算法的全局搜索能力,加快了收敛速度;而且也改善了求解的质量及其优化结果的可靠性,是求解优化问题的一种有潜力的算法.     

An Improved Immune Genetic Algorithm for Solving the Optimization Problems of Computer Communication Networks

1　IntroductionIndesigningacomputercommunicationnet work ,thenetworkaveragedelayisanimportantpa rameterinthenetworkperformance .Inthispaper,weonlyconsiderM /M/1networks,whichmeansthatthemessageprocessingtimeisaprobabilisticdensityfunctionwithnegativepower,thegroupar rivalandsendingisofPoissiondistributionwithasinglequeue .Supposethatthenetworktopologicalstructureandtheestimatesoftheexternaltrafficrequirementsaregiven ,howtoselecttheoptimalroutestobeusedbythecommunicatingnodesinthenetworksoast…     

基于多算子协同进化的自适应并行量子遗传算法

量子遗传算法具有种群规模小,全局搜索能力强的特点被广泛应用于各类优化问题的求解.为了进一步提高量子遗传算法的收敛速度和搜索稳定性,克服算法的早熟问题,本文改进了基于自适应机制的量子遗传算法.在自适应量子遗传算法的基础上根据种群的适应度定义了个体相似度评价算子、个体适应度评价算子和种群变异调整算子及相应算子的计算方法,利用多算子协同评价当前种群状态并根据进化代数的变化,自适应的改变个体的变异概率,提高了算法全局寻优能力和收敛速度,降低了算法陷入局部寻优的概率.此外,为了提高算法的时间效率,将算法采用并行多宇宙的方式实现.实验结果表明,本文提出的算法在全局搜索性能、收敛速度和时间效率方面有较好的综合表现.     

基于增殖系数的混沌信号提取算法

本文提出一种基于混沌信号特性的信号盲提取算法,由于不同的混沌信号在相空间里面对应着不同的吸引子二阶增长率,利用这个特点定义了增殖系数（Proliferation Exponent,PE）并将其作为混沌信号提取的目标函数.首先分析基于增殖系数的梯度搜索方法在解决盲提取问题时存在不足,并将混沌信号的盲提取问题转化为带约束的优化问题,提出利用改进的粒子群优化算法解决信号盲提取的优化问题,通过惯性系数动态调整和最优位置的扰动,提高算法的寻优性能.实验结果表明基于增殖系数的信号提取算法能有效地提取混沌信号,提取的信号在时域和相空间与源信号接近,同时算法也表现出对噪声污染的鲁棒性.     

Synthesis of Resonator Filters With Arbitrary Topology Using Hybrid Method

This paper presents a new synthesis method for resonator filters of arbitrary topology using an evolutionary hybrid method. This method consists of a Levenberg-Marquardt algorithm for a local optimizer and genetic algorithm for a global optimizer, respectively. Unlike the conventional hybrid method in which the local optimization is performed after finding appropriate initial values from global optimization, the local optimizer in the proposed method is used as a genetic-algorithm operator to prevent trapping in local minima of the cost function. This method can provide fast convergence and good accuracy to find the final solution from initial population generated by a random number and the known value for the filters with stringent requirements. In addition, multiple coupling matrices to meet the given requirement can be obtained from the initial population based on a random number. Resonator filters with asymmetric eight-pole configurations for single and dual passbands are synthesized using the current method for validation. Excellent agreement between the response computed from characteristic polynomials and the response computed from couplings is obtained from the proposed method.     

An Improved Genetic Algorithm with Quasi-Gradient Crossover

The convergence of genetic algorithm is mainly determined by its core operation crossover operation. When the objective function is a multiple hump function, traditional genetic algorithms are easily trapped into local optimum, which is called premature conver- gence. In this paper, we propose a new genetic algorithm with improved arithmetic crossover operation based on gradient method. This crossover operation can generate offspring along quasi-gradient direction which is the Steepest descent direction of the value of objective function. The selection operator is also simplified, every individual in the population is given an opportunity to get evolution to avoid complicated selection algorithm. The adaptive mutation operator and the elitist strategy are also applied in this algorithm. The case 4 indicates this algorithm can faster converge to the global optimum and is more stable than the conventional genetic algorithms.     

Genetic algorithms for finite mixture model based voxel classification in neuroimaging

Finite mixture models (FMMs) are an indispensable tool for unsupervised classification in brain imaging. Fitting an FMM to the data leads to a complex optimization problem. This optimization problem is difficult to solve by standard local optimization methods, such as the expectation-maximization (EM) algorithm, if a principled initialization is not available. In this paper, we propose a new global optimization algorithm for the FMM parameter estimation problem, which is based on real coded genetic algorithms. Our specific contributions are two-fold: 1) we propose to use blended crossover in order to reduce the premature convergence problem to its minimum and 2) we introduce a completely new permutation operator specifically meant for the FMM parameter estimation. In addition to improving the optimization results, the permutation operator allows for imposing biologically meaningful constraints to the FMM parameter values. We also introduce a hybrid of the genetic algorithm and the EM algorithm for efficient solution of multidimensional FMM fitting problems. We compare our algorithm to the self-annealing EM-algorithm and a standard real coded genetic algorithm with the voxel classification tasks within the brain imaging. The algorithms are tested on synthetic data as well as real three-dimensional image data from human magnetic resonance imaging, positron emission tomography, and mouse brain MRI. The tissue classification results by our method are shown to be consistently more reliable and accurate than with the competing parameter estimation methods.     

基于改进混沌遗传算法的无人机航迹规划

如何快速地规划出满足约束条件的飞行航迹,是实现无人机自主规划的关键。提出了一种基于混沌遗传算法的航迹规划方法,该方法首先由Voronoi图生成初始航迹,然后采用混沌遗传算法在生成的航迹空间中寻优。主要对近年来出现的混沌遗传算法进行了改进以使其更具智能化。该方法采用幂函数载波代替传统混沌优化算法中的线性载波;为进一步提高混沌映射迭代序列的均匀性,提出了确定区间的随机幂指数概念并将其应用到混沌遗传算法中。仿真结果表明,该方法可以提高混沌遗传算法收敛的精确性。     

一种求解冰壶比赛对阵多约束问题的逐层优化算法

冰壶比赛对阵编排问题是一个难于收敛的多约束优化问题.为此提出一种求解此类问题的逐层优化的单亲遗传算法.首先将待求解问题的多个约束进行分层;其次设计了靶向自交叉算子进行第一层优化以提高搜索效率,设计了定点-随机自交叉算子进行第二层优化以保持种群的多样性;最后,将改进的算法用于解决冰壶比赛对阵编排的多约束优化问题,构建了该问题的适应度函数.仿真实验表明,与粒子群算法和经典遗传算法相比,所提算法能够有效求解冰壶比赛对阵编排的多约束优化问题.     

DOA and Power Estimation Using Genetic Algorithm and Fuzzy Discrete Particle Swarm Optimization

Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival (DOA). In this method, genetic algorithm (GA) and fuzzy discrete particle swarm optimization (FDPSO) are applied to optimize the direction of arrival and power parameters of the mode simultaneously. Firstly, the GA algorithm is applied to make the solution fall into the global searching. Secondly, the FDPSO method is utilized to narrow down the search field. In FDPSO, chaotic factor and crossover method are added to speed up the convergence. This approach has been demonstrated through some computational simulations. It is shown that the proposed algorithm can estimate both the DOA and the powers accurately. It is more efficient than some present methods, such as Newton-like algorithm, Akaike information critical (AIC), particle swarm optimization (PSO), and genetic algorithm with particle swarm optimization (GA-PSO).     

A hybrid genetic algorithm and chaotic function model for image encryption

The security of digital images has attracted much attention recently. In this study, a new method based on a hybrid model is proposed for image encryption. The hybrid model is composed of a genetic algorithm and a chaotic function. In the first stage of the proposed method, a number of encrypted images are constructed using the original image and the chaotic function. In the next stage, these encrypted images are used as the initial population for the genetic algorithm. In each stage of the genetic algorithm, the answer obtained from the previous iteration is optimized to produce the best-encrypted image. The best-encrypted image is defined as the image with the highest entropy and the lowest correlation coefficient among adjacent pixels.