一种基于遗传算法优化的大数据特征选择方法

提出了一种基于遗传算法的大数据特征选择算法。该算法首先对各维度的特征进行评估,根据每个特征在同类最近邻和异类最近邻上的差异度调整其权重,基于特征权重引导遗传算法的搜索,以提升算法的搜索能力和获取特征的准确性;然后结合特征权重计算特征的适应度,以适应度作为评价指标,启动遗传算法获取最优的特征子集,并最终实现高效准确的大数据特征选择。通过实验分析发现,该算法能够有效减小分类特征数,并提升特征分类准确率。     

信号处理中去噪算法的改进仿真

针对信号处理中交替投影法去噪计算量大的缺点,提出了改进方法,改进后的方法首先利用Ad hoc算法求出信号的模极大值,然后对模极大值点分段进行抛物线插值,从而得到信号的估计小波系数,最后利用估计小波系数重构信号,实现去噪的目的。仿真实验证明：该方法能有效去除噪声,与交替投影法相比,该算法在原理上更简单,程序实现更容易,去噪速度更快,是更加实用的方法,特别是便于用硬件实现。     

基于新的森林优化算法的特征选择算法

针对传统的基于森林优化算法的特征选择算法在初始化阶段、候选森林生成阶段和更新阶段存在的问题,提出了一种新的基于森林优化算法的特征选择算法。该算法在初始化阶段采用皮尔森相关系数和L1正则化方法代替随机初始化策略;在候选森林生成阶段,采用优劣树分开和差额补足的方法解决优劣树不完备问题;在更新阶段,将与最优树精度相同但维度不同的树木添加到森林中。在实验中,所提算法采用与传统的基于森林优化算法的特征选择算法相同的实验数据和实验参数,分别测试了小维度、中维度和大维度数据。实验结果表明,在2个大维度数据和2个中维度数据上,所提算法的分类精度和维度缩减能力均高于传统的基于森林优化算法的特征选择算法。实验结果验证了所提算法在处理特征选择问题的有效性。     

一种盲信号处理中的时域自适应滤波算法

滤波是信号处理中的重要环节,鉴于盲信号处理本身的特点,传统的滤波技术并不适合直接用于盲源分离之中。然而作为分离前的预处理,滤波技术在独立成分分离算法中是必要的。为此,本文结合稳健的数据非线性投影,首次提出盲信号中的自适应滤波方法,与此同时给出了具有自适应特性的阈值判决。在此基础上构造了盲信号中的自适应滤波算法,解决了利用低通和高通滤波处理盲信号所遇到的问题。仿真结果表明,在不破坏数据统计特性的前提下,该方法能有效滤除数据中的野值成分,避免了野值数据对独立成分分离算法性能的影响,为盲信号分离的预处理开辟了一种新的途径。     

Baldwinian learning in clonal selection algorithm for optimization

Artificial immune systems are a kind of new computational intelligence methods which draw inspiration from the human immune system. Most immune system inspired optimization algorithms are based on the applications of clonal selection and hypermutation, and known as clonal selection algorithms. These clonal selection algorithms simulate the immune response process based on principles of Darwinian evolution by using various forms of hypermutation as variation operators. The generation of new individuals is a form of the trial and error process. It seems very wasteful not to make use of the Baldwin effect in immune system to direct the genotypic changes. In this paper, based on the Baldwin effect, an improved clonal selection algorithm, Baldwinian Clonal Selection Algorithm, termed as BCSA, is proposed to deal with optimization problems. BCSA evolves and improves antibody population by four operators, clonal proliferation, Baldwinian learning, hypermutation, and clonal selection. It is the first time to introduce the Baldwinian learning into artificial immune systems. The Baldwinian learning operator simulates the learning mechanism in immune system by employing information from within the antibody population to alter the search space. It makes use of the exploration performed by the phenotype to facilitate the evolutionary search for good genotypes. In order to validate the effectiveness of BCSA, eight benchmark functions, six rotated functions, six composition functions and a real-world problem, optimal approximation of linear systems are solved by BCSA, successively. Experimental results indicate that BCSA performs very well in solving most of the test problems and is an effective and robust algorithm for optimization.     

筛选和记忆相结合的粒子群算法*

针对粒子群算法优化高维复杂问题出现局部最优的缺陷,提出初始粒子筛选和最差粒子记忆相结合的粒子群算法。利用熵度量粒子分量分布的均匀性,只有各分量满足均匀性要求时,该粒子才被筛选为初始粒子,以控制粒子在解空间的分布。在速度更新过程中引入最差粒子,避免粒子重复搜索曾经找到的最差位置,以提高算法的搜索效率。根据粒子寻优的成功率动态调整权重,以有效平衡深度和广度搜索能力。用本文算法优化6个经典测试函数,与3种改进的PSO算法相比,本文提出的算法不仅可以平衡局部和全局的搜索能力,还可以提高算法的搜索效率和精度。     

用于函数优化和特征选择的翻筋斗觅食海鸥优化算法

针对基本海鸥优化算法（SOA）在处理复杂优化问题中存在低精度、慢收敛和易陷入局部最优的不足,提出了一种基于翻筋斗觅食策略的SOA算法（SFSOA）。该算法首先采用基于倒S型函数的控制参数A非线性递减策略更新海鸥个体的位置,以改善个体的质量和加快收敛速度;引入一种基于翻筋斗觅食策略的学习机制以增加海鸥个体位置的多样性,避免算法在搜索后期陷入局部最优值。选取八个基准函数优化问题进行数值实验,并与基本SOA、灰狼优化算法和改进SOA进行比较,结果表明,所提算法具有较高的解精度、较快的收敛速度和较强的全局搜索能力,能有效地处理复杂函数优化问题。最后,将SFSOA用于求解特征选择问题,获得了满意的结果。     

改进迁移算子的BBO算法及其在PID参数中的优化

针对生物地理学优化（BBO）算法寻优过程中易陷入搜索动力不足、收敛精度不高等问题,提出一种基于改进迁移算子的生物地理学优化算法（IMO-BBO）。在BBO算法基础上,结合“优胜劣汰”的进化思想,将迁移距离作为影响因素对迁移算子进行改进,并用差分策略将不适宜迁移的个体进行替换,以增加算法的局部探索能力。同时为丰富物种的多样性,引入多种群概念。利用IMO-BBO算法分别对13个基准测试函数进行测试,与基于协方差迁移算子和混合差分策略的BBO （CMM-DE/BBO）算法和BBO算法相比,改进算法提高了对全局最优解的搜索能力,在收敛速度和精确度上也都有显著提高;将IMO-BBO算法应用到PID参数整定中,仿真结果表明,所提算法优化后的控制器具有更快的响应速度和更稳定的精度。     

空间自适应免疫克隆选择优化算法

针对免疫克隆选择优化算法晚期收敛速度慢的不足,通过引入搜索空间自适应缩放的思想,提出一种新的空间自适应免疫克隆选择优化算法(SAIS)。算法利用不完全演化搜索优化解的分布特性,以精英个体为中心收缩搜索空间,并采用空间扩张机制帮助算法跳出局部最优。通过对高维基准测试函数实验表明,SAIS能显著提高收敛速度和优化解的质量。     

函数优化的遗传算法策略优选

为了提高函数优化的准确性和效率,提出一种基于表达式构造的函数聚类和策略优选的方法.使用英国Sheffield大学开发的Matlab遗传算法工具箱(GATBX)设计不同的算法策略,对随机选取的3种常见的函数构造因子按不同比例组合得到的不同模式进行了策略试算,以收敛率、平均截止代数及截止代数分布熵作为由主到次的性能评价指标来优选策略,并归纳出规则.最后利用4个具有试验模式的数值函数验证了规则的有效性.     

The signal selection and processing method for polarization measurement radar

Based on the ambiguity function, a novel signal processing method for the polarization measurement radar is developed. One advantage of this method is that the two orthogonal polarized signals do not have to be perpendicular to each other, which is required by traditional methods. The error due to the correlation of the two transmitting signals in the traditional method, can be reduced by this new approach. A concept called ambiguity function matrix (AFM) is introduced based on this method. AFM is a promisi...     

Seeker optimization algorithm for tuning the structure and parameters of neural networks

Seeker optimization algorithm (SOA) is a novel population-based heuristic stochastic search algorithm, which is based on the concept of simulating the act of human searching. In the SOA, the search direction is determined by seeker’s egotistic behavior, altruistic behavior and pro-activeness behavior, while step length is given by uncertainty reasoning behavior. In this paper, the application of the SOA to tuning the structures and parameters of artificial neural networks (ANNs) is presented as a new evolutionary method of ANN training. Simulation experiments for pattern classification and function approximation are performed. The comparisons of the SOA between BP algorithms and other evolutionary algorithms (EAs) are studied. The simulation results show that the performance of the SOA is better than or, at least, equivalent to that of other EAs (i.e., DE and two variations of PSO) for all the listed problems. Moreover, the ANNs with link switches trained by the SOA can provide better or comparable learning capabilities with much less number of links than ones by BP algorithms (i.e., GDX, RP, OSS and SCG). Hence, SOA can simultaneously tune the structures and the weight values, and, though SOA is more computationally intensive, it is believed that SOA will become a promising candidate for training ANNs.     

基于自学习算法的BCG信号处理方法

针对心冲击描记(BCG)信号中心跳波形缺乏确定模型且受多种因素影响的特点,提出了基于自学习的心跳识别算法.使用聚类分析的方法,有效提取了BCG信号中具有高度相关性的一组曲线,并将其作为心跳模型.将心跳模型与BCG信号进行匹配,捕捉到心跳信号进而得到心跳周期.经实验验证:算法得出的心跳周期误差在±2%以内,并在Android平台上进一步验证了其准确性和实用性.     

具有振荡约束的自然选择萤火虫优化算法

针对基本萤火虫算法高维求解精度低、收敛速度慢、易早熟等缺点,提出一种具有振荡、约束和自然选择机制的萤火虫算法,引入二阶振荡因子,平衡上一代个体对当前代个体的影响,防止萤火虫个体陷入局部极值;加入基于sigmoid函数的约束因子,动态调整个体移动距离,在算法后期避免萤火虫个体在理论最优值附近因过度扰震而导致精度降低的情况;采用基于高斯积分倒数递减趋势的自然选择,在保持个体多样性的同时加快算法的收敛速度.理论分析证明了改进算法的收敛性和时间复杂度.通过对10个不同特征标准测试函数多个维度的函数优化仿真实验,测试结果表明改进算法的寻优精度和收敛速度均有明显提升,尤其是在高维情况下,几乎对于所有函数仍能找到理论最优解,较好地解决了萤火虫算法不适于高维求解的问题.     

A framework for computation-memory algorithmic optimization for signal processing

The heterogeneity of today's computing environment means computation-intensive signal processing algorithms must be optimized for performance in a machine dependent fashion. In this paper, we present a dynamic memory model and associated optimization framework that finds a machine-dependent, near-optimal implementation of an algorithm by exploiting the computation-memory tradeoff. By optimal, we mean an implementation that has the fastest running time given the specification of the machine memory hierarchy. We discuss two instantiations of the framework: fast IP address lookup, and fast nonuniform scalar quantizer and unstructured vector quantizer encoding. Experiments show that both instantiations outperform techniques that ignore this computation-memory tradeoff.     

贝叶斯优化算法的选择策略分析

针对贝叶斯优化算法的选择策略问题,对变量无关,双变量相关,多变量相关等3类典型函数分别用锦标赛选择、截断选择和比例选择以及自适应比例选择进行了实验。建立了相应的贝叶斯网络概率模型,并分析指出锦标赛选择策略能有效保持样本的多样性,并能建立起准确的网络模型。与比例选择策略和截断选择策略相比较,该选择策略更适用于贝叶斯优化算法。     

A multi-objective optimization algorithm for feature selection problems

Feature selection (FS) is a critical step in data mining, and machine learning algorithms play a crucial role in algorithms performance. It reduces the processing time and accuracy of the categories. In this paper, three different solutions are proposed to FS. In the first solution, the Harris Hawks Optimization (HHO) algorithm has been multiplied, and in the second solution, the Fruitfly Optimization Algorithm (FOA) has been multiplied, and in the third solution, these two solutions are hydride and are named MOHHOFOA. The results were tested with MOPSO, NSGA-II, BGWOPSOFS and B-MOABC algorithms for FS on 15 standard data sets with mean, best, worst, standard deviation (STD) criteria. The Wilcoxon statistical test was also used with a significance level of 5% and the Bonferroni–Holm method to control the family-wise error rate. The results are shown in the Pareto front charts, indicating that the proposed solutions' performance on the data set is promising.

     

基于克隆进化选择算法的多目标优化研究

把免疫系统的克隆选择学说与生物进化法则应用到多目标优化计算中,引入免疫克隆学说的记忆单元体,使用聚类方法对其中的抗体进行不断的优化更新和劣体淘汰;采用非均匀变异操作促进种群抗体的多样性;通过抗体间亲和度体现种群中个体的竞争,抗体与抗原亲和度来抑制过度的竞争,维持种群广泛性.最后由计算机仿真实验,并与NSGA-Ⅱ算法比较了两者的收敛性和分布性,证明由克隆进化算法得到的结果距离真实Pareto曲线更接近,分布更均匀、范围更广泛.     

盲均衡技术在地震信号反褶积中的应用

为了消除地震反射系数的影响,有效提取地震子波,提出一种基于最小均方误差准则的SIMO系统的盲均衡算法,该算法利用接收数据的截短协方差矩阵设计了直接均衡器,该方法是盲均衡算法在地震信号处理中的应用,避免传统反褶积运算中对称正定矩阵的限制,从而改善了算法的稳定性并提高收敛速度。计算机仿真表明,新算法可获得较优的地震子波,从而再利用地震子波对反射系数进行分析,并且具有很快的收敛速度。