一种改进的基于遗传算法的聚类分析方法

C－均值聚类收敛速度快，但是它容易陷入局部最优，且对初始解很敏感。遗传算法是一种全局搜索方法，但是它收敛速度慢。为了在搜索能力和收敛速度两方面都取得较好的效果，本文提出了一种改进的基于遗传算法的聚类分析方法。实验结果表明：本文提出的算法在聚类分析中搜索到全局最优解（或近似全局最优解）的能力要优于经典遗传算法及C－均值聚类算法；且通过对变异概率的巧妙设置，提高了算法的自适应能力。     

新收敛性分析方法在机器人路径规划中的应用

应用齐次有限马尔科夫链为工具，给出基本遗传算法（SGAS）的收敛法分析方法。并得出基本遗传算法不是全面收敛的，最优保存简单遗传算法（MOSGAS）是全面收敛的结论。并利用该方法规划机器人行走路径。     

基于分层竞争的高效并行遗传算法

借鉴生态进化中普遍存在的竞争现象，本文提出了一种基于多层竞争的并行遗传算法（GAMLC,genetic algorithm based on multi-level competition）。仿真结果表明，该算法能较好地解决遗传算法中的早熟现象及收敛速度慢的问题，提高了搜索到全局最优解的概率。     

遗传操作的一般性算子及图象恢复处理

针对遗传算法中 收剑问题,设计了一种遗传操作的一般性算子,该算子能够：（１）判断群体多样性的好坏程度,以使决定交叉和变异谁是主要、辅助算子;（２）当 某代群体的多样性差时,随机独立地产生多个新种个体;（３）当接近最优解邻域时,加速向最优解收敛。本文结合退化图象的,提出子基于算子的衅象遗传恢复方法,计算机模拟和实验结果表明,该方法能够较好地解决简单遗传算法中过早收敛于非全局最佳恢复图象问题。     

基于小生境遗传算法的移动机器人路径优化

针对标准遗传算法存在收敛性慢和局部最优解的缺陷,结合移动机器人行走特点,提出一种基于预选择机制小生境技术的改进遗传算法中移动机器人路径规划方法.该方法兼顾对局部最优解和全局最优解的搜索,维持群体的多样性,避免了早期收敛现象的发生;同时也增强了自然群体进化的并行性,加快了搜索进程.计算机仿真结果表明,该算法在收敛速度和输出全局最优解概率方面相对于标准遗传算法有了显著提高.     

基于遗传算法的梳状谱峰值控制方法

针对梳状谱峰值控制问题，采取遗传算法（GA）搜索梳状谱峰值的最小值。通过构造相位集进行编码，采用移民和精英保留使得结果收敛到最优解，实验结果表明此方法比穷举法效果好且耗时短。     

基于自识别交叉算子和自适应变异算子的遗传算法研究

为有效地解决遗传算法收敛速度和局部最优解的矛盾,本文提出了一种具有自识别交叉算子和基于海明距离的动态变异算子的遗传算法。自识别交叉算子保证父代的优良模式遗传到下一代,加快了算法的收敛速度;而动态变异算子扩大了搜索范围,增强了算法跳离局部最优解的能力。实验证明,两种改进算子的有效结合保证算法能以较快速度收敛于全局最优解。     

基于自识别交叉算子和自适应变异算子的遗传算法研究

为有效地解决遗传算法收敛速度和局部最优解的矛盾,本文提出了一种具有自识别交叉算子和基于海明距离的动态变异算子的遗传算法.自识别交叉算子保证父代的优良模式遗传到下一代,加快了算法的收敛速度;而动态变异算子扩大了搜索范围,增强了算法跳离局部最优解的能力.实验证明,两种改进算子的有效结合保证算法能以较快速度收敛于全局最优解.     

基于改进遗传算法的图像边缘检测

针对经典边缘检测算法在一定程度上的局限性,提出了一种改进的遗传算法进行边缘检测。阐述了图像梯度的幅值和变化率,并将遗传算法引入到图像边缘检测中。利用天牛须搜索算法来引导遗传算法中交叉点位置的移动方向。通过提高子辈种群的多样性,防止了搜索区域逐渐缩小,克服了传统遗传算法早熟收敛的现象。实验结果表明,与传统的边缘检测算法相比,改进的遗传算法应用于不同的图像检测具有良好的稳定性和较快的收敛速度,可以收敛到全局最优解甚至逼近全局最优解,从而提高被检测图像边缘的完整性。     

基于IAFSA和AGA混合算法的移动机器人路径规划

针对人工鱼群算法在移动机器人路径规划中存在易陷入局部最优、结果精度不高以及遗传算法存在易早熟、收敛速度慢等问题,提出一种改进人工鱼群算法(IAFSA)和自适应遗传算法(AGA)相融合的移动机器人路径规划方法。首先用栅格法建立移动机器人的环境模型,然后用IAFSA搜索移动机器人的初始可行路径,将搜索到的初始可行路径作为AGA的初始种群,最后采用AGA优化移动机器人的全局最优路径。仿真结果表明,混合算法在结果精度和稳定性方面优于标准人工鱼群算法,在跳出局部最优和收敛速度方面优于标准遗传算法。     

一种非线性回归模型参数估计的混合基因算法

提出了一种新的结合可变多面体法和基因算法的混合基因算法(HGA),它通过对问题的解空间交替进行全局和局部搜索,达到快速收敛至全局最优解,较好地解决了基因算法在达到全局最优解前收敛慢的问题。非线性回归模型参数估计的实验表明该算法具有较好的通用性和有效性。     

A new block-based motion estimation algorithm

The conventional motion estimation algorithms used in digital television coding can roughly be classified into two categories, namely the block-matching method and the recursive method. Each of them has its own strong points. In this paper, a new type of block-based motion estimation algorithm is presented, which is based on the block-recursive (gradient) method and makes use of some of the merits of the block-matching method. For a moderate translational motion, motion estimation with a subpel precision can conveniently be obtained with only a couple of recursive searches, and for a violent or complicated motion which cannot be estimated by any block-based algorithm, the local minimum of prediction errors can always be found. Our experiments show that the proposed algorithm is efficient and reliable, and obviously superior to the conventional block-recursive algorithms and the fast block-matching algorithms. The performance of the proposed algorithm tends almost to the optimum of the full search algorithm with the same estimation precision, but the computational effort is much less than that of the full search algorithm.     

基于边界约束的样品视频非对称运动估计方法

针对常规运动估计方法应用到样品视频编码时存在无效搜索点冗余搜索、有效搜索点遗漏的问题,提出基于边界约束的非对称运动估计方法。首先,在原有预定搜索范围的基础上,采用定点统计运动参量的方法对科学仪器的样品视频运动性能进行测试统计,根据统计结果,对视频运动范围设定边界,减少运动搜索点数;然后提出了基于边界约束的非对称搜索模型,依据样品视频的运动特征,优化搜索算法。来自电子探针和电子显微镜的不同样品视频编码实验表明,与多方向搜索算法(MDS)比较,所提方法的运动估计时间缩短了约33%,编码性能保持甚至超过了多方向搜索算法。     

采用DIRECT算法的外辐射源雷达高效直接定位方法

针对直接定位目标函数为非凸函数,传统凸优化方法难以获得全局最优解,而常用网格遍历搜索方法运算量大的问题,提出采用DIRECT (Dividing rectangles)算法进行高效求解的方法。首先建立外辐射源雷达接收信号模型,在此基础上推导最大似然直接定位目标函数,为解决该目标函数非凸难以快速获得全局最优解的问题,将DIRECT算法用于目标位置的快速估计, 并理论分析其计算复杂度。数值仿真表明新方法计算速度快且定位精度高,相比网格遍历法,计算时间降低2个数量级,相比遗传算法降低1个数量级。      

Application of natural computing algorithms to maximum likelihood estimation of direction of arrival

This work presents a study of the performance of populational meta-heuristics belonging to the field of natural computing when applied to the problem of direction of arrival (DOA) estimation, as well as an overview of the literature about the use of such techniques in this problem. These heuristics offer a promising alternative to the conventional approaches in DOA estimation, as they search for the global optima of the maximum likelihood (ML) function in a framework characterized by an elegant balance between global exploration and local improvement, which are interesting features in the context of multimodal optimization, to which the ML-DOA estimation problem belongs. Thus, we shall analyze whether these algorithms are capable of implementing the ML estimator, i.e., finding the global optima of the ML function. In this work, we selected three representative natural computing algorithms to perform DOA estimation: differential evolution, clonal selection algorithm, and the particle swarm. Simulation results involving different scenarios confirm that these methods can reach the performance of the ML estimator, regardless of the number of sources and/or their nature. Moreover, the number of points evaluated by such methods is quite inferior to that associated with a grid search, which gives support to their application.     

一种基于区域搜索的全局运动估计与补偿算法

开展全局运动估计与补偿研究是进行动态目标检测中的基础和前提。在总结现有运动估计与补偿方法的基础上,提出一种基于图像分割区域的运动性和大小的全局运动估计与补偿算法。首先,通过建立区域搜索的全局运动模型,同时进行区域定性分割和区域大小排序;然后,根据误差最小化准则在指定的分割区域中进行线性递归搜索,利用门限准则寻找出最佳的运动估计参数;最后,根据双线性内插法获得运动位移量。实验结果比较可知,所提算法较三步搜索算法(TSS)和全局搜索算法(FSA)等传统算法具有更高的准确性(图像平移帧差)和实时性(算法运行时间),能够很好地实现运动背景的全局校正。     

A hybrid evolutionary programming method for circuit optimization

A hybrid evolutionary programming (EP) method is presented for global optimization of complex circuits. The conventional EP is integrated with a clustering algorithm to improve the robustness of the algorithm for complex multimodal circuit optimization problems. The EP generates populations around the regions of the search space which can potentially contain a minimum but may be overlooked. The clustering algorithm is used to identify these regions dynamically. In order to improve the speed of optimization, the EP is combined with a gradient-based search method in an efficient fashion. The local search is performed from the center of each identified cluster in order to find the minimum in the region very fast. The hybrid algorithm can also reduce the search space by avoiding the search in the areas that were previously investigated. This feature greatly improves the speed of optimization and prevents the premature convergence as well. The algorithm performed very well in several benchmark problems including a test function minimization and global optimization of a complex RF diplexer circuit.     

An efficient search strategy for block motion estimation usingimage features

Block motion estimation using the exhaustive full search is computationally intensive. Fast search algorithms offered in the past tend to reduce the amount of computation by limiting the number of locations to be searched. Nearly all of these algorithms rely on this assumption: the mean absolute difference (MAD) distortion function increases monotonically as the search location moves away from the global minimum. Essentially, this assumption requires that the MAD error surface be unimodal over the search window. Unfortunately, this is usually not true in real-world video signals. However, we can reasonably assume that it is monotonic in a small neighborhood around the global minimum. Consequently, one simple strategy, but perhaps the most efficient and reliable, is to place the checking point as close as possible to the global minimum. In this paper, some image features are suggested to locate the initial search points. Such a guided scheme is based on the location of certain feature points. After applying a feature detecting process to each frame to extract a set of feature points as matching primitives, we have extensively studied the statistical behavior of these matching primitives, and found that they are highly correlated with the MAD error surface of real-world motion vectors. These correlation characteristics are extremely useful for fast search algorithms. The results are robust and the implementation could be very efficient. A beautiful point of our approach is that the proposed search algorithm can work together with other block motion estimation algorithms. Results of our experiment on applying the present approach to the block-based gradient descent search algorithm (BBGDS), the diamond search algorithm (DS) and our previously proposed edge-oriented block motion estimation show that the proposed search strategy is able to strengthen these searching algorithms. As compared to the conventional approach, the new algorithm, through the extraction of image features, is more robust, produces smaller motion compensation errors, and has a simple computational complexity.     

基于量子克隆选择的自适应多模式快速运动估计算法

该文提出一种新的基于量子克隆选择的自适应多模式快速运动估计算法。算法利用序列图像的时空预测运动矢量作为序列活动剧烈程度的依据,自适应选择搜索模式。静止块直接中止搜索;平缓运动类型块以(0,0)点为起始点,直接使用CDS搜索模式;剧烈运动类型块使用QCS/CDS联合搜索模式,首先使用量子克隆选择QCS寻找近似最优解,然后以近似最优解为起始点,使用CDS搜索。实验结果证明,算法在能够获得接近全搜索方法所得到的平均峰值信噪比前提下,平均搜索点数大大减少,搜索速度显著加快。     

时-空二维信号模型下的目标参数估计

提出一种基于时空二维信号模型下相干信号源参数估计的LS-ESPRIT算法,解决了常规ESPRIT算法不能解相干等问题。和解相干的MUSIC算法相比,该方法不需要在整个空间进行谱峰搜索,运算量小。仿真结果表明,该方法适用于所有信号(包括非相干和相干信号)的目标二维参数与多普勒频率估计。在不同信噪比(SNR)情况下,其估计精度较常规方法有了较大的提高,可以满足工程应用的需要。