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1.
Salp Swarm Algorithm (SSA) is one of the most recently proposed algorithms driven by the simulation behavior of salps. However, similar to most of the meta-heuristic algorithms, it suffered from stagnation in local optima and low convergence rate. Recently, chaos theory has been successfully applied to solve these problems. In this paper, a novel hybrid solution based on SSA and chaos theory is proposed. The proposed Chaotic Salp Swarm Algorithm (CSSA) is applied on 14 unimodal and multimodal benchmark optimization problems and 20 benchmark datasets. Ten different chaotic maps are employed to enhance the convergence rate and resulting precision. Simulation results showed that the proposed CSSA is a promising algorithm. Also, the results reveal the capability of CSSA in finding an optimal feature subset, which maximizes the classification accuracy, while minimizing the number of selected features. Moreover, the results showed that logistic chaotic map is the optimal map of the used ten, which can significantly boost the performance of original SSA.  相似文献   

2.
Salp Swarm Algorithm (SSA) is a novel swarm intelligent algorithm with good performance. However, like other swarm-based algorithms, it has insufficiencies of low convergence precision and slow convergence speed when dealing with high-dimensional complex optimisation problems. In response to this concerning issue, in this paper, we propose an improved SSA named as WASSA. First of all, dynamic weight factor is added to the update formula of population position, aiming to balance global exploration and local exploitation. In addition, in order to avoid premature convergence and evolution stagnation, an adaptive mutation strategy is introduced during the evolution process. Disturbance to the global extremum promotes the population to jump out of local extremum and continue to search for an optimal solution. The experiments conducted on a set of 28 benchmark functions show that the improved algorithm presented in this paper displays obvious superiority in convergence performance, robustness as well as the ability to escape local optimum when compared with SSA.  相似文献   

3.
Neural Computing and Applications - Salp swarm algorithm (SSA) is a newly swarm-based metaheuristic algorithm that simulate the swimming and foraging behaviour of salps in oceans so to search for...  相似文献   

4.
吕佳 《计算机工程与设计》2007,28(24):6039-6041
特征提取算法TFIDF是文本分类中常用的衡量特征权重的算法,但该算法没有考虑特征词在类间和类内的分布情况,导致算法无法反映特征词在分布比例中量上的差异.为此,引入方差来描述特征词在类间和类内的分布情况,并利用方差来修正TFIDF权重.仿真实验结果表明,同传统TFIDF算法相比,改进TFIDF算法能得到更好的分类结果.  相似文献   

5.
为了解决中文文本分类中初始特征空间维数过高带来的“维数灾难”问题,提高分类精度和分类效率,提出了一种基于模拟退火及蜂群算法的优化特征选择算法.该算法中,以蜂群算法流程为主体,根据蜜蜂群体觅食的特点快速寻找最优解,并且针对蜂群算法容易陷入局部最优解的问题,把模拟退火算法机制引入其中.该算法既保留了蜂群算法群体寻优的特点,又可以有效地避免陷入局部最优解.通过选择合适的收益率函数和温度下降函数,用实验的方法与卡方统计、信息增益和互信息等算法进行比较,表明了该算法的可行性和有效性.  相似文献   

6.
The feature selection process constitutes a commonly encountered problem of global combinatorial optimization. This process reduces the number of features by removing irrelevant, noisy, and redundant data, thus resulting in acceptable classification accuracy. Feature selection is a preprocessing technique with great importance in the fields of data analysis and information retrieval processing, pattern classification, and data mining applications. This paper presents a novel optimization algorithm called catfish binary particle swarm optimization (CatfishBPSO), in which the so-called catfish effect is applied to improve the performance of binary particle swarm optimization (BPSO). This effect is the result of the introduction of new particles into the search space (“catfish particles”), which replace particles with the worst fitness by the initialized at extreme points of the search space when the fitness of the global best particle has not improved for a number of consecutive iterations. In this study, the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) was used to evaluate the quality of the solutions. CatfishBPSO was applied and compared to 10 classification problems taken from the literature. Experimental results show that CatfishBPSO simplifies the feature selection process effectively, and either obtains higher classification accuracy or uses fewer features than other feature selection methods.  相似文献   

7.
Feature selection is a useful pre-processing technique for solving classification problems. The challenge of solving the feature selection problem lies in applying evolutionary algorithms capable of handling the huge number of features typically involved. Generally, given classification data may contain useless, redundant or misleading features. To increase classification accuracy, the primary objective is to remove irrelevant features in the feature space and to correctly identify relevant features. Binary particle swarm optimization (BPSO) has been applied successfully to solving feature selection problems. In this paper, two kinds of chaotic maps—so-called logistic maps and tent maps—are embedded in BPSO. The purpose of chaotic maps is to determine the inertia weight of the BPSO. We propose chaotic binary particle swarm optimization (CBPSO) to implement the feature selection, in which the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) serves as a classifier for evaluating classification accuracies. The proposed feature selection method shows promising results with respect to the number of feature subsets. The classification accuracy is superior to other methods from the literature.  相似文献   

8.
Neural Computing and Applications - A Gaussian based Particle Swarm Optimization Gravitational Search Algorithm (GPSOGSA) is being proposed for extensive feature selection that serves highly in...  相似文献   

9.
Wang  Zongshan  Ding  Hongwei  Yang  Zhijun  Li  Bo  Guan  Zheng  Bao  Liyong 《Applied Intelligence》2022,52(7):7922-7964
Applied Intelligence - Salp swarm algorithm (SSA) is a relatively new and straightforward swarm-based meta-heuristic optimization algorithm, which is inspired by the flocking behavior of salps when...  相似文献   

10.
数据库通常包含很多冗余特征,找出重要特征叫做特征提取。本文提出一种基于属性重要度的启发式特征选取算法。该算法以属性重要度为迭代准则得到属性集合的最小约简。  相似文献   

11.
针对K-means对初始聚类中心敏感和易陷入局部最优的缺点,提出了一种改进的基于粒子群的聚类算法.该算法结合基于密度和最大最小距离法来确定初始聚类中心,解决K-means对初始值敏感的问题;利用粒子群算法全局寻优能力强的优点,避免K-means陷入局部最优.通过对样本集各维属性的规范化处理,惯性权值采用凹函数递减,计算相异度矩阵,引入用群体适应度方差,进一步优化混合算法.实验结果表明,该算法具有更高的准确率和更强的收敛能力.  相似文献   

12.
The text clustering technique is an appropriate method used to partition a huge amount of text documents into groups. The documents size affects the text clustering by decreasing its performance. Subsequently, text documents contain sparse and uninformative features, which reduce the performance of the underlying text clustering algorithm and increase the computational time. Feature selection is a fundamental unsupervised learning technique used to select a new subset of informative text features to improve the performance of the text clustering and reduce the computational time. This paper proposes a hybrid of particle swarm optimization algorithm with genetic operators for the feature selection problem. The k-means clustering is used to evaluate the effectiveness of the obtained features subsets. The experiments were conducted using eight common text datasets with variant characteristics. The results show that the proposed algorithm hybrid algorithm (H-FSPSOTC) improved the performance of the clustering algorithm by generating a new subset of more informative features. The proposed algorithm is compared with the other comparative algorithms published in the literature. Finally, the feature selection technique encourages the clustering algorithm to obtain accurate clusters.  相似文献   

13.
针对传统卡方统计量(CHI)方法在全局范围内进行特征选择时忽略词频信息问题,提出了一种改进的文本特征选择方法。通过引入特征分布相关性系数,选择局部出现的强相关性特征,并利用修正因子解决CHI方法的负相关困扰,从而提升语料集的分类指标。对网易新闻语料库和复旦大学中文语料库进行实验时,利用以上方法进行特征选择,使用改进后的词频—逆文本频率(TF-IDF)权重计算公式加权,分类器选择支持向量机(SVM)和朴素贝叶斯法。结果表明:改进的方法不仅在分类效果上有明显的提高,而且性能更加稳定。  相似文献   

14.
针对传统樽海鞘群算法寻优精度低、易于陷入局部最优的问题,提出基于混沌映射与动态学习的自适应樽海鞘群算法.引入改进混沌Tent映射实现种群初始化,确保更加均匀的搜索空间;设计基于Logistic映射的领导者更新机制,有效增强种群多样性;利用基于动态学习的追随者更新机制,使算法跳出局部最优,提升全局搜索能力;设计领导者/追...  相似文献   

15.
《微型机与应用》2017,(23):62-65
对经典DV-Hop算法误差比较大的现象进行讨论。对DV-Hop算法进行改进,提出BSADV-Hop算法。该算法分为两大部分,第一部分对跳数计算方法进行改进;第二部分对平均每跳距离进行寻优。在这过程中采用一种群体智能算法——鸟群算法最终降低平均每跳距离导致的误差。仿真实验结果证明,与经典DV-Hop和PSODV-Hop相比,该算法能更准确地计算节点平均跳距,定位精度得以提高,并且体现出较好的稳定性和可行性。  相似文献   

16.
针对传统的模糊C-均值聚类算法对初始聚类中心较敏感、易陷入局部最优的缺点,将粒子群优化算法和FCM算法相结合,提出一种改进的模糊聚类算法。该算法利用粒子群算法的全局搜索能力代替FCM算法寻找初始聚类中心,使其跳出局部最优,实现模糊聚类。主要从反映数据集分类的类内紧致性程度和类间分离性程度的角度考虑,重新设计适应度函数。实验结果表明,提出的算法在聚类正确率和有效性指标上有更好的效果。  相似文献   

17.
Xu  Ruohao  Li  Mengmeng  Yang  Zhongliang  Yang  Lifang  Qiao  Kangjia  Shang  Zhigang 《Applied Intelligence》2021,51(10):7233-7244

Feature selection is a technique to improve the classification accuracy of classifiers and a convenient data visualization method. As an incremental, task oriented, and model-free learning algorithm, Q-learning is suitable for feature selection, this study proposes a dynamic feature selection algorithm, which combines feature selection and Q-learning into a framework. First, the Q-learning is used to construct the discriminant functions for each class of the data. Next, the feature ranking is achieved according to the all discrimination functions vectors for each class of the data comprehensively, and the feature ranking is doing during the process of updating discriminant function vectors. Finally, experiments are designed to compare the performance of the proposed algorithm with four feature selection algorithms, the experimental results on the benchmark data set verify the effectiveness of the proposed algorithm, the classification performance of the proposed algorithm is better than the other feature selection algorithms, meanwhile the proposed algorithm also has good performance in removing the redundant features, and the experiments of the effect of learning rates on the our algorithm demonstrate that the selection of parameters in our algorithm is very simple.

  相似文献   

18.
针对基于粒子群的模糊聚类算法以隶属度编码时对噪音敏感,以及处理样本数小于样本维数的数据集效果较差等问题,通过改进其中的模糊聚类约束方法,提出一种改进的基于粒子群的模糊聚类方法.当样本对各类的隶属度之和不为1时,新方法在粒子群优化得出的隶属度基础上,根据样本与各类之间的距离对隶属度进一步分配,以使隶属度满足模糊聚类约束条件.新方法显著地改善了在隶属度编码下使用粒子群进行模糊聚类的效果,并通过典型的数据集进行了验证.  相似文献   

19.
基于改进Canny特征点的SIFT算法   总被引:1,自引:0,他引:1  
杨秋菊  肖雪梅 《计算机工程与设计》2011,32(7):2428-2431,2458
在分析了传统SIFT算法的基础上,提出了一种基于改进Canny特征点的SIFT算法。该方法先用SIFT算法检测出候选关键点,再利用Canny边缘检测算法检测出图像的边缘点,通过比较生成的候选关键点和边缘点的坐标是否相等,从而判断是否去除候选关键点。实验结果表明,与传统SIFT算法比较,该方法可以有效地去除DOG算子生成的边缘响应点,进一步提高SIFT特征点的抗噪声能力,增强SIFT算法的稳定性。  相似文献   

20.
基于锦标赛选择遗传算法的随机微粒群算法   总被引:1,自引:0,他引:1  
以保证全局收敛的随机微粒群算法SPSO为基础。提出了一种改进的随机微粒群算法-GAT-SPSO。该方法是在SPSO的进化过程中.以锦标赛选择机制下的遗传算法所产生的最优个体来代替SPSO中停止的微粒,参与下一代的群体进化。通过时三个多峰的测试函数进行仿真,其结果表明:在搜索空间维数相同的情况下,GAT-SPSO的收敛率厦收敛速度均大大优于SPSO。  相似文献   

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