改进的樽海鞘群算法

针对樽海鞘群算法在对函数优化问题求解上出现的求解精度不高、收敛速度慢的缺点,提出了一种改进的群海鞘群算法.对于领导者引入加权重心取代最优个体位置,防止过早聚集在最优个体附近;对于追随者引入自适应惯性权重平衡算法的全局搜索和局部寻优能力;最后对于个体进行逐维随机差分变异,减少维间干扰,提高了种群的多样性.仿真实验结果表明改进的樽海鞘群算法在均值、标准差和收敛曲线优于标准樽海鞘群算法和其他改进算法,说明改进后的算法提高了寻优性能,有较高的求解精度和较快的收敛速度.     

基于混合策略改进的樽海鞘群算法

针对樽海鞘群算法(SSA)在求解复杂优化问题时存在的易陷入局部最优、收敛精度低等缺点,提出一种基于混合策略改进的樽海鞘群算法(ISSA).首先,采用Sobol序列实现樽海鞘种群的初始化,使初始种群在解空间中分布更加均匀,进而提高算法的全局寻优能力;其次,在领导者位置更新阶段引入步长控制因子,根据不同寻优时期自动调节领导者的搜索范围,有效平衡算法的全局搜索与局部搜索;然后,采用改进的透镜成像策略对领导者进行映射,避免算法陷入局部最优;此外,在追随者位置更新阶段,引入一种自主选择追随机制,改善追随者的盲从性,以提高算法的收敛精度;最后,与其他几种代表性优化算法在12个基准测试函数上进行仿真实验对比,并进行Wilcoxon秩和检验,实验结果表明所提出ISSA在收敛速度和精度上有明显提升,相较于其他优化算法具有更好的寻优效果和稳定性.另外,通过两个工程设计案例实验进行测试,进一步验证了所提出ISSA的可行性和适用性.     

A novel chaotic salp swarm algorithm for global optimization and feature selection

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.     

基于衰减因子和动态学习的改进樽海鞘群算法

樽海鞘群算法是一种新型的群智能优化算法.与其他智能优化算法相比,樽海鞘群算法的优化求解策略仍有待改进,以进一步提高该算法的求解精度和寻优效率.本文提出一种基于衰减因子和动态学习的改进樽海鞘群算法,通过在领导者更新阶段添加衰减因子,提高算法的局部开发能力,在跟随者更新阶段引入动态学习策略,提高算法的全局搜索能力.本文对16个测试函数进行实验,将提出的改进算法与其他智能优化算法比较,实验结果表明,本文提出的改进算法在收敛精度和收敛速度方面有较大提升,具有良好的优化性能.     

多策略融合的改进樽海鞘群算法

为解决传统樽海鞘群算法(SSA)收敛精度低、难以跳出局部最优等问题,提出了一种多策略融合的改进樽海鞘群算法(ISSA)。首先,提出了一种新的融合中垂线算法收敛策略的追随者位置更新方法,以解决传统SSA追随者位置更新方法的不足;为提升SSA跳出局部最优的能力,提出一种基于中垂线算法收敛策略的自扰动策略。其次,通过分析传统SSA领导者位置更新策略存在的不足,提出了一种新的领导者位置更新策略,并针对SSA的固定种群顺序,提出了以适应度为指标重构樽海鞘群体排列顺序的方法以提升算法性能。最后以仿真实验对ISSA的性能进行了验证,结果表明ISSA解决了SSA收敛精度低和难以跳出局部最优的问题,提升了SSA的收敛速度和稳定性。通过与其他改进SSA的对比实验,证明了ISSA的优越性。     

Improved salp swarm algorithm based on weight factor and adaptive mutation

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.     

一种基于粒子群优化的目标跟踪特征选择算法

针对复杂背景下的运动目标跟踪特征选择问题,提出了一种基于粒子群优化的目标跟踪特征选择算法。假设具有目标与背景间最好可分离性的特征为最好的跟踪特征。通过构建目标与背景的图像特征分布方差的比值函数作为衡量目标与背景间的可分离性判据。使用粒子群优化算法优化不同的特征组合实时获取最优的目标跟踪特征。为验证该算法的有效性,将选择的最优特征与一种基于核的跟踪算法相结合进行跟踪实验。实验结果表明,算法能有效提高传统基于核的跟踪算法对于复杂场景下的运动目标跟踪的鲁棒性与准确性。     

基于自适应正态云模型的引力樽海鞘群算法

针对樽海鞘群算法(salp swarm algorithm,SSA)在求解复合问题时存在收敛速度慢和容易陷入局部最优等缺点,提出一种结合引力搜索技术与正态云发生器的樽海鞘群算法(cloud gravitational SSA,CGSSA).在更新樽海鞘领导者位置阶段引入引力搜索算法(gravitational sear...     

Modified salp swarm algorithm for global optimisation

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...     

改进信息增益的维吾尔文特征选择方法

特征选择是维吾尔语文本分类的关键技术,对分类结果将产生直接的影响。为了提高传统信息增益在维吾尔文特征选择中的效果,在深度分析维吾尔文语种特点的基础上,提出了一种新的信息增益特征选择方法。该方法结合类词频和特征分布系数以及倒逆文档频率,对传统信息增益进行修正;引入一个备选特征分布系数来平衡类间选取的特征个数;在维吾尔文数据集上实验验证。实验结果表明,改进的算法对维吾尔文分类效果有明显的提高。     

基于信息熵的改进TFIDF特征选择算法

特征的选择对文本分类的精确性有着非常重要的影响。针对传统的TFIDF没有考虑特征词条在各个类之间的分布的不足,对TFIDF特征选择算法进行了深入的分析,并结合信息熵的概念提出了一种新的TFIDF特征选择算法。实验结果表明,改进后的算法可以有效地提高文本分类的精确度。     

Improved particle swarm optimization algorithm and its application in text feature selection

Text feature selection is an importance step in text classification and directly affects the classification performance. Classic feature selection methods mainly include document frequency (DF), information gain (IG), mutual information (MI), chi-square test (CHI). Theoretically, these methods are difficult to get improvement due to the deficiency of their mathematical models. In order to further improve effect of feature selection, many researches try to add intelligent optimization algorithms into feature selection method, such as improved ant colony algorithm and genetic algorithms, etc. Compared to the ant colony algorithm and genetic algorithms, particle swarm optimization algorithm (PSO) is simpler to implement and can find the optimal point quickly. Thus, this paper attempt to improve the effect of text feature selection through PSO. By analyzing current achievements of improved PSO and characteristic of classic feature selection methods, we have done many explorations in this paper. Above all, we selected the common PSO model, the two improved PSO models based respectively on functional inertia weight and constant constriction factor to optimize feature selection methods. Afterwards, according to constant constriction factor, we constructed a new functional constriction factor and added it into traditional PSO model. Finally, we proposed two improved PSO models based on both functional constriction factor and functional inertia weight, they are respectively the synchronously improved PSO model and the asynchronously improved PSO model. In our experiments, CHI was selected as the basic feature selection method. We improved CHI through using the six PSO models mentioned above. The experiment results and significance tests show that the asynchronously improved PSO model is the best one among all models both in the effect of text classification and in the stability of different dimensions.     

文本分类中基于方差的改进特征提取算法

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.     

改进的基于粒子群优化的支持向量机特征选择和参数联合优化算法

针对支持向量机(SVM)中特征选择和参数优化对分类精度有较大影响,提出了一种改进的基于粒子群优化(PSO)的SVM特征选择和参数联合优化算法(GPSO-SVM),使算法在提高分类精度的同时选取尽可能少的特征数目。为了解决传统粒子群算法在进行优化时易出现陷入局部最优和早熟的问题,该算法在PSO中引入遗传算法(GA)中的交叉变异算子,使粒子在每次迭代更新后进行交叉变异操作来避免这一问题。该算法通过粒子之间的不相关性指数来决定粒子之间的交叉配对,由粒子适应度值的大小决定其变异概率的大小,由此产生新的粒子进入到群体中。这样使得粒子跳出当前搜索到的局部最优位置,提高了群体的多样性,在全局范围内寻找更优值。在不同数据集上进行实验,与基于PSO和GA的特征选择和SVM参数联合优化算法相比,GPSO-SVM的分类精度平均提高了2%~3%,选择的特征数目减少了3%~15%。实验结果表明,所提算法的特征选择和参数优化效果更好。     

基于改进邻域空间的高维混合数据特征选择算法

作为数据挖掘领域中一项重要的数据预处理技术,特征选择算法能够有效应对高维数据带来的“维数灾难”问题.然而,如何对高维的混合数据进行特征选取仍然是当前研究的重点和难点之一.基于邻域关系的邻域粗糙集模型因其能够处理名词型属性与数值型属性并存的混合数据,已成功应用于混合数据的特征选择.但是,现有邻域粗糙集对混合数据邻域关系的度量,仍然是基于等价关系的名词型数据划分与基于相似关系的数值型数据划分的简单融合,在利用模型划分的邻域空间和预定义的评价函数对高维混合数据进行特征选取时,适应性较差.为此,在邻域粗糙集模型的基础上,提出一种改进的邻域空间构造方法,并设计相应的邻域空间度量公式作为判别指标,自适应地调节邻域空间下邻域粒的大小;为了准确地表征高维混合数据邻域空间的判别能力,设计一种考虑边界数据和邻域空间大小的评价函数;在此基础上,提出一种启发式的高维混合数据特征选择算法.通过UCI标准数据集验证所提出算法的有效性.     

面向全局搜索的自适应领导者樽海鞘群算法

针对实际配送过程中客户需求、车辆服务时间随机可变,提出带软时间窗的随机需求和随机服务时间的车辆路径问题.以配送车辆行驶路径为研究对象,建立基于配送成本、时间惩罚成本、修正成本的配送车辆路径优化模型,并提出一种混合禁忌搜索算法.该算法将最近邻算法和禁忌搜索算法相结合,将时间窗宽度及距离作为最近邻算法中节点选择标准;并对禁忌搜索算法中禁忌长度等构成要素进行自适应调整,引入自适应惩罚系数.实验结果表明,改进后的混合禁忌搜索算法具有较强的寻优能力、较高的鲁棒性,同时算法所得车辆行驶路径受客户需求变动影响较小.     

基于模拟退火及蜂群算法的优化特征选择算法

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.     

基于高斯扰动和自然选择的改进粒子群优化算法

为了有效地平衡粒子群算法的全局与局部搜索性能,提出一种基于高斯扰动和自然选择的改进粒子群优化算法。该算法在采用简化粒子群优化算法的基础上,考虑到个体最优粒子间的相互影响,使用所有融入高斯扰动的个体最优的平均值代替每个粒子的个体最优值,并且借鉴自然选择中适者生存的进化机制提高算法优化性能;同时通过含有惯性权重停止阈值的自适应调节余弦函数递减策略来实现对惯性权重的非线性调整并采用异步变化调整策略来改善粒子的学习能力。仿真实验结果表明,所提算法在收敛速度和精度等方面均有提高,寻优性能优于近期文献中的几种改进的粒子群优化算法。     

基于种群特征反馈的布谷鸟搜索算法

布谷鸟搜索(CS) 算法是一种新型的生物启发式算法. 为了提高算法对不同优化问题的适应能力, 根据反馈控制原理提出一种基于种群特征反馈的布谷鸟搜索(SFFCS) 算法, 将年龄结构、变异成功率等种群特征作为反馈信息引入算法框架, 动态调节算法参数, 同时引入双进化策略机制和策略选择概率, 加强算法对局部搜索和全局搜索的平衡能力. 对标准测试函数和电力系统最优潮流问题进行数值实验, 实验结果表明, SFFCS 算法具有较好的收敛性能和适应能力, 验证了所提出算法的有效性和工程应用价值.

     

Improved binary particle swarm optimization using catfish effect for feature selection

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.