参数选取对遗传算法动力学形态的影响

遗传算法中参数的选取决定遗传算法的运行性能．目前,对算法中参数选取都是经验性的．本文针对一个典型的2-bit问题,分析了在不同参数选取下GA的全局动力学形态．通过对标准遗传算法的各种参数的选取,分别建立了数学模型．分析了这些模型的吸引子,揭示了不同参数对动力学形态的影响．世代重叠模型和无参数模型的动力学形态相似．当变异概率很小时,模型与没有变异算子相类似;当变异算子足够大时,模型的动力学形态随着变异概率的增加发生了突变．原有的吸引不动点消失,原来的排斥不动点变成吸引不动点．这些论证为遗传算法中参数选取提供了一些理论上的证据．     

多智能体社会进化算法的动力学分析

通过一个简化的2-bit问题对多智能体社会进化算法(MASEA)中的进化算子及其组合进行形式化描述,分析了MASEA的全局动力学形态。针对算法中的进化算子建立数学模型,通过分析模型中各个不动点的吸引性,揭示出不同进化算子对动力学形态的影响,证明了算法MASEA的全局收敛性。     

一种可自适应调节参数的改进遗传算法

针对遗传算法在复杂问题应用中收敛速度十分缓慢的不足，本文引入收敛性因子和进程因子对种群进化的交叉概率和变异概率进行自适应调节，提出了可自适应调节参数的改进遗传算法．该算法很好地增强了遗传算法的全局搜索能力，提高了收敛速度．通过比较几个优化实例，验证了本文算法的有效性．     

一种快速收敛的遗传算法

为了解决遗传算法的收敛速度和全局收敛性之间的矛盾，提出了一种新的快速收敛的改进遗传算法。该改进算法设计了与个体适应度相关的变异算子，以及与早熟情况、进化代数和个体适应度有关的移民算法。实例验证表明，该改进遗传算法在收敛速度和获取全局最优解的概率两个方面都有很大的提高。     

精英策略个体优势遗传算法研究

针对遗传算法存在的局部搜索能力差、早熟收敛和进化后期收敛速度慢的问题,提出了一种改进精英策略的个体优势遗传算法（Individual Advantages Genetic Algorithm,IAGA）。IAGA通过在精英子种群更新中不断增加精英个体数量和多样性,在保持算法全局收敛性的同时,增强算法在最优解区域的局部搜索能力。引入半粒子群变异算子,提高了算法前期向全局最优解靠拢的速度;引入个体优势算子,提高种群优势个体的多样性,有效改善了进化后期收敛速度慢的问题;与已有同类算法相比,平衡了收敛速度和全局收敛性之间矛盾的同时,进一步提高了收敛速度和精度。     

引入模拟退火算子的差分进化算法性能研究

为了提高差分进化算法的优化性能,将模拟退火算子引入到差分进化算法中,利用模拟退火算子良好的全局搜索能力进一步提高差分进化算法对复杂问题的优化能力．通过对复杂函数优化的仿真结果表明,算法在求解复杂优化问题上具有更快的收敛速度和更好的全局收敛性．     

基于遗传算法求解应急决策系统中的最优路径

提出了一种将模拟退火算法和遗传算法相结合的进化算法GASA,利用Boltzmann机制 接收交叉和变异后的个体,避免遗传算法中存在的早熟收敛问题,增强了算法的全局收敛性,并对遗 传算子(选择、交叉、变异算子)进行重构,引入新的交叉算子和变异算子能根据种群的进化情况动态 调整遗传算子,加速进化后期搜索效率。实验表明,将此算法用于应急决策系统的最优路径的求解中 与传统算法相比,能加速进化速度和全局寻优能力,提高应急决策效率。     

计算Banach不动点的进化策略算法

针对目前计算Banach不动点的迭代算法存在着收敛性和性能特征在很大程度上依赖于初始点和计算过程因串行运行造成效率低等问题,提出了应用进化策略算法来计算Banach不动点的并行算法,充分发挥了进化策略算法的群体搜索和全局收敛的特性,快速的并行搜索,有效地克服了经典Banach不动点的迭代算法初始点敏感和效率低的问题.数值计算结果表明该算法收敛速度快、精度高、鲁棒性强,为计算Banach不动点提供了一种可行的方法.     

基于竞争方程的竞争型协同进化算法及应用

协同进化算法是近年来针对遗传算法的不足而兴起的，还处于研究初步阶段。本文在竞争型协同进化的基础上，借鉴生态学中种群竞争的Gause竞争模型，提出了Gause竞争型协同进化模型及算法，并将该算法应用于模糊神经系统的辨识问题上。实验证明，该算法比标准遗传算法、典型竞争型协同进化算法和BP学习算法具有更好的全局收敛性和更快的收敛速度，它在一定程度上解决了标准遗传算法的不足。     

一种求解高维优化问题的多目标遗传算法及其收敛性分析

单纯Pareto遗传算法很难解决目标数目很多的高维多目标优化问题，在多个指标之间引入偏好信息，提出的多目标遗传算法使进化群体按协调模型进行偏好排序，改变了传统的基于Pareto优于关系来比较个体的优劣。另外讨论了算法在满足一定条件下具有全局收敛性，典型算例的数学解析和实验验证了其具有较好的收敛性和收敛速度．     

Particle swarm optimization with mutation operations controlled by landscape modality estimation using hill-valley detection

Particle swarm optimization (PSO) is one of swarm intelligence algorithms and has been used to solve various optimization problems. Since the performance of PSO is much affected by the algorithm parameters of PSO, studies on adaptive control of the parameters have been done. Adaptive PSO (APSO) is one of representative studies. Parameters are controlled according to the evolutionary state, where the state is estimated by distance relations among a best search point and other search points. Also, a global Gaussian mutation operation is introduced to escape from local optima. In this study, a new adaptive control based on landscape modality estimation using hill-valley detection is proposed. A proximity graph is created from search points, hills and valleys are detected in the graph, landscape modality of an objective function is identified as unimodal or multimodal. Parameters are adaptively controlled as: parameters for convergence are selected in unimodal landscape and parameters for divergence are selected in multimodal landscape. Also, two mutation operations are introduced according to the modality. In unimodal landscape, a new local mutation operation is applied to the worst hill point which will be moved toward the best point for convergence. In multimodal landscape, a new adaptive global mutation operation is applied to all hill points for escaping from local optima. The advantage of the proposed method is shown by comparing the results of the method with those by PSO with fixed parameters and APSO.     

Snake pedals: compact and versatile geometric models withphysics-based control

We introduce a geometric shape modeling scheme which allows for representation of global and local shape characteristics of an object. Geometric models are well-suited for representing global shapes without local detail, but we propose a scheme which represents global shapes with local detail and permits model shaping as well as topological changes via physics-based control. The scheme represents shapes by pedal curves and surfaces, i.e. the loci of the foot of perpendiculars to the tangents of a fixed curve/surface from a fixed point called the pedal point. By varying the location of the pedal point, one can synthesize a large class of shapes which exhibit both local and global deformations. We introduce physics-based control for shaping these geometric models by letting the pedal point vary and use a snake to represent the position of this varying point. The model, a “snake pedal”, allows for interactive manipulation via forces applied to the snake. We develop a fast numerical iterative algorithm for shape recovery from image data using this scheme. The algorithm involves the Levenberg-Marquardt (LM) method in the outer loop for solving the global parameters and the alternating direction implicit (ADI) method in the inner loop for solving the local parameters of the model. The combination of the global and local scheme leads to an efficient numerical solution to the model fitting problem. We demonstrate the applicability of this modeling scheme via examples of shape synthesis and shape estimation from real image data     

遗传算法收敛性的动力学分析及其应用

遗传算法的收敛性，特别是交叉算子的作用，一直缺乏深入的理论分析，当系统动力学的方法被应用于遗传算法的运行机理分析时，可以探讨在没有变异算子情况下遗传算法的收敛性问题，从而，明确了局部极值点的含义，指出了局部极值点的存在性和存在条件，证明了遗传算法在局部极值点附近的收敛性，并针对遗传算法的各种改进给出了理论上的依据，提出了遗传算法改进的方向。     

Fixed point iteration in identifying bilinear models

Inspired by fixed point theory, an iterative algorithm is proposed to identify bilinear models recursively in this paper. It is shown that the resulting iteration is a contraction mapping on a metric space when the number of input–output data points approaches infinity. This ensures the existence and uniqueness of a fixed point of the iterated function sequence and therefore the convergence of the iteration. As an application, one class of block-oriented systems represented by a cascade of a dynamic linear (L), a static nonlinear (N) and a dynamic linear (L) subsystems is illustrated. This gives a solution to the long-standing convergence problem of iteratively identifying LNL (Winer–Hammerstein) models. In addition, we extend the static nonlinear function (N) to a nonparametric model represented by using kernel machine.     

太阳电池I-V曲线拟合的优化算法

通过对遗传算法(GA)和人工鱼群算法(AFSA)的研究,结合太阳电池I-V曲线的数学模型,提出了一种遗传算法与人工鱼群算法相互融合的优化算法(GA-AFSA)。GA-AFSA保持了遗传算法的全局寻优的优点,克服了人工鱼群漫无目的随机游动和遗传算法收敛慢的缺点,并且通过人工鱼群算法的计算提高了收敛速度。利用了太阳电池实测数据进行I-V曲线拟合及太阳电池的光生电流、二极管品质因数、串联电阻、反向饱和电流、并联电阻等5个重要参数的最优求解。将GA-AFSA与已有的算法进行了比较,仿真实验表明GA-AFSA精度高,收敛速度快。     

Robust point matching for nonrigid shapes by preserving local neighborhood structures

In previous work on point matching, a set of points is often treated as an instance of a joint distribution to exploit global relationships in the point set. For nonrigid shapes, however, the local relationship among neighboring points is stronger and more stable than the global one. In this paper, we introduce the notion of a neighborhood structure for the general point matching problem. We formulate point matching as an optimization problem to preserve local neighborhood structures during matching. Our approach has a simple graph matching interpretation, where each point is a node in the graph, and two nodes are connected by an edge if they are neighbors. The optimal match between two graphs is the one that maximizes the number of matched edges. Existing techniques are leveraged to search for an optimal solution with the shape context distance used to initialize the graph matching, followed by relaxation labeling updates for refinement. Extensive experiments show the robustness of our approach under deformation, noise in point locations, outliers, occlusion, and rotation. It outperforms the shape context and TPS-RPM algorithms on most scenarios.     

基于改进迭代收缩阈值算法的微观3D重建方法

迭代收缩阈值算法（ISTA）求解离焦深度恢复动态优化问题时,采用固定迭代步长,导致算法收敛效率不佳,使得重建的微观3D形貌精度不高。为此,提出一种基于加速算子梯度估计和割线线性搜索的方法优化ISTA——FL-ISTA。首先,在每一次迭代中,由当前点和前一个点的线性组合构成加速算子重新进行梯度估计,更新迭代点;其次,为了改变迭代步长固定的限制,引入割线线性搜索,动态确定每次最优迭代步长;最后,将改进的迭代收缩阈值算法用于求解离焦深度恢复动态优化问题,加快算法的收敛速度、提高微观3D形貌重建的精度。在对标准500 nm尺度栅格的深度信息重建实验中,与ISTA、快速ISTA （FISTA）和单调快速ISTA （MFISTA）相比,FL-ISTA收敛速度均有所提升,重建的深度信息值下降了10个百分点,更接近标准500 nm栅格尺度;与ISTA相比,FL-ISTA重建的微观3D形貌均方差（MSE）和平均误差分别下降了18个百分点和40个百分点。实验结果表明,FL-ISTA有效提升了求解离焦深度恢复动态优化问题的收敛速度,提高了微观3D形貌重建的精度。