An efficient algorithm for high-dimensional function optimization

To solve high-dimensional function optimization problems, many evolutionary algorithms have been proposed. In this paper, we propose a new cooperative coevolution orthogonal artificial bee colony (CCOABC) algorithm in an attempt to address the issue effectively. Cooperative coevolution frame, a popular technique in evolutionary algorithms for large scale optimization problems, is adopted in this paper. This frame decomposes the problem into several subcomponents by random grouping, which is a novel decomposition strategy mainly for tackling nonseparable functions. This strategy can increase the probability of grouping interacting variables in one subcomponent. And for each subcomponent, an improved artificial bee colony (ABC) algorithm, orthogonal ABC, is employed as the subcomponent optimizer. In orthogonal ABC, an Orthogonal Experimental Design method is used to let ABC evolve in a quick and efficient way. The algorithm has been evaluated on standard high-dimensional benchmark functions. Compared with other four state-of-art evolutionary algorithms, the simulation results demonstrate that CCOABC is a highly competitive algorithm for solving high-dimensional function optimization problems.     

A whale optimization algorithm based on quadratic interpolation for high-dimensional global optimization problems

Whale Optimization Algorithm (WOA), as a new population-based optimization algorithm, performs well in solving optimization problems. However, when tackling high-dimensional global optimization problems, WOA tends to fall into local optimal solutions and has slow convergence rate and low solution accuracy. To address these problems, a whale optimization algorithm based on quadratic interpolation (QIWOA) is presented. On the one hand, a modified exploration process by introducing a new parameter is proposed to efficiently search the regions and deal with the premature convergence problem. On the other hand, quadratic interpolation around the best search agent helps QIWOA to improve the exploitation ability and the solution accuracy. Moreover, the algorithm tries to make a balance between exploitation and exploration. QIWOA is compared with several state-of-the-art algorithms on 30 high-dimensional benchmark functions with dimensions ranging from 100 to 2000. The experimental results show that QIWOA has faster convergence rate and higher solution accuracy than both WOA and other population-based algorithms. For functions with a flat or sharp bottom, QIWOA is difficult to find the global optimum, but it still performs best compared with other algorithms.     

Hybrid binary arithmetic optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical data

Gene expression data play a significant role in the development of effective cancer diagnosis and prognosis techniques. However, many redundant, noisy, and irrelevant genes (features) are present in the data, which negatively affect the predictive accuracy of diagnosis and increase the computational burden. To overcome these challenges, a new hybrid filter/wrapper gene selection method, called mRMR-BAOAC-SA, is put forward in this article. The suggested method uses Minimum Redundancy Maximum Relevance (mRMR) as a first-stage filter to pick top-ranked genes. Then, Simulated Annealing (SA) and a crossover operator are introduced into Binary Arithmetic Optimization Algorithm (BAOA) to propose a novel hybrid wrapper feature selection method that aims to discover the smallest set of informative genes for classification purposes. BAOAC-SA is an enhanced version of the BAOA in which SA and crossover are used to help the algorithm in escaping local optima and enhancing its global search capabilities. The proposed method was evaluated on 10 well-known microarray datasets, and its results were compared to other current state-of-the-art gene selection methods. The experimental results show that the proposed approach has a better performance compared to the existing methods in terms of classification accuracy and the minimum number of selected genes.

     

A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detection

Feature selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. In this article, a new hybrid model of whale optimization algorithm (WOA) and flower pollination algorithm (FPA) is presented for the problem of FS based on the concept of opposition‐based learning (OBL) which name is HWOAFPA. The procedure is that the WOA is run first and at the same time during the run, the WOA population is changed by the OBL. And, to increase the accuracy and speed of convergence, it is used as the initial population of FPA. To evaluate the performance of the proposed method, experiments were carried out in two steps. The experiments were performed on 10 datasets from the UCI data repository and Email spam detection datasets. The results obtained from the first step showed that the proposed method was more successful in terms of the average size of selection and classification accuracy than other basic metaheuristic algorithms. In addition, the results from the second step showed that the proposed method which was a run on the Email spam dataset performed much more accurately than other similar algorithms in terms of accuracy of Email spam detection.     

一种面向高维数据的均分式Lasso特征选择方法

Lasso是一种基于一范式的特征选择方法。与已有的特征选择方法相比较,Lasso不仅能够准确地选择出与类标签强相关的变量,同时还具有特征选择的稳定性,因而成为人们研究的一个热点。但是,Lasso方法与其他特征选择方法一样,在高维海量或高维小样本数据集的特征选择容易出现计算开销过大或过学习问题（过拟和）。为解决此问题,提出一种改进的Lasso方法：均分式Lasso方法。均分式Lasso方法将特征集均分成K份,对每份特征子集进行特征选择,将每份所选的特征进行合并,再进行一次特征选择。实验表明,均分式Lasso方法能够很好地对高维海量或高维小样本数据集进行特征选择,是一种有效的特征选择方法。     

A new improved whale optimization algorithm with joint search mechanisms for high-dimensional global optimization problems

Similar to other swarm-based algorithms, the recently developed whale optimization algorithm (WOA) has the problems of low accuracy and slow convergence. It is also easy to fall into local optimum. Moreover, WOA and its variants cannot perform well enough in solving high-dimensional optimization problems. This paper puts forward a new improved WOA with joint search mechanisms called JSWOA for solving the above disadvantages. First, the improved algorithm uses tent chaotic map to maintain the diversity of the initial population for global search. Second, a new adaptive inertia weight is given to improve the convergence accuracy and speed, together with jump out from local optimum. Finally, to enhance the quality and diversity of the whale population, as well as increase the probability of obtaining global optimal solution, opposition-based learning mechanism is used to update the individuals of the whale population continuously during each iteration process. The performance of the proposed JSWOA is tested by twenty-three benchmark functions of various types and dimensions. Then, the results are compared with the basic WOA, several variants of WOA and other swarm-based intelligent algorithms. The experimental results show that the proposed JSWOA algorithm with multi-mechanisms is superior to WOA and the other state-of-the-art algorithms in the competition, exhibiting remarkable advantages in the solution accuracy and convergence speed. It is also suitable for dealing with high-dimensional global optimization problems.

     

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.     

Toward an efficient and scalable feature selection approach for internet traffic classification

There is significant interest in the network management and industrial security community about the need to identify the “best” and most relevant features for network traffic in order to properly characterize user behaviour and predict future traffic. The ability to eliminate redundant features is an important Machine Learning (ML) task because it helps to identify the best features in order to improve the classification accuracy as well as to reduce the computational complexity related to the construction of the classifier. In practice, feature selection (FS) techniques can be used as a preprocessing step to eliminate irrelevant features and as a knowledge discovery tool to reveal the “best” features in many soft computing applications. In this paper, we investigate the advantages and disadvantages of such FS techniques with new proposed metrics (namely goodness, stability and similarity). We continue our efforts toward developing an integrated FS technique that is built on the key strengths of existing FS techniques. A novel way is proposed to identify efficiently and accurately the “best” features by first combining the results of some well-known FS techniques to find consistent features, and then use the proposed concept of support to select a smallest set of features and cover data optimality. The empirical study over ten high-dimensional network traffic data sets demonstrates significant gain in accuracy and improved run-time performance of a classifier compared to individual results produced by some well-known FS techniques.     

一种改进的鲸鱼优化算法

针对鲸鱼优化算法（whale optimization algorithm ,WOA）容易陷入局部最优和收敛精度低的问题进行了研究,提出一种改进的鲸鱼优化算法（IWOA）。该算法通过准反向学习方法来初始化种群,提高种群的多样性;然后将线性收敛因子修改为非线性收敛因子,有利于平衡全局搜索和局部开发能力;另外,通过增加自适应权重改进鲸鱼优化算法的局部搜索能力,提高收敛精度;最后,通过随机差分变异策略及时调整鲸鱼优化算法,避免陷入局部最优。实验选取九个基准函数,所有算法均迭代30次,结果表明：改进的鲸鱼优化与原鲸鱼优化算法以及五种改进的鲸鱼优化算法相比,其均值和标准差均优于其他算法,收敛曲线也优于其他大多数算法。说明改进的鲸鱼优化算法收敛精度和算法稳定性最佳,收敛速度较其他大多数改进的鲸鱼优化算法明显加快。     

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.

     

A hybrid algorithm for feature subset selection in high-dimensional datasets using FICA and IWSSr algorithm

Feature subset selection is a substantial problem in the field of data classification tasks. The purpose of feature subset selection is a mechanism to find efficient subset retrieved from original datasets to increase both efficiency and accuracy rate and reduce the costs of data classification. Working on high-dimensional datasets with a very large number of predictive attributes while the number of instances is presented in a low volume needs to be employed techniques to select an optimal feature subset. In this paper, a hybrid method is proposed for efficient subset selection in high-dimensional datasets. The proposed algorithm runs filter-wrapper algorithms in two phases. The symmetrical uncertainty (SU) criterion is exploited to weight features in filter phase for discriminating the classes. In wrapper phase, both FICA (fuzzy imperialist competitive algorithm) and IWSSr (Incremental Wrapper Subset Selection with replacement) in weighted feature space are executed to find relevant attributes. The new scheme is successfully applied on 10 standard high-dimensional datasets, especially within the field of biosciences and medicine, where the number of features compared to the number of samples is large, inducing a severe curse of dimensionality problem. The comparison between the results of our method and other algorithms confirms that our method has the most accuracy rate and it is also able to achieve to the efficient compact subset.     

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。     

A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection

Matrix computations are both fundamental and ubiquitous in computational science, and as a result, they are frequently used in numerous disciplines of scientific computing and engineering. Due to the high computational complexity of matrix operations, which makes them critical to the performance of a large number of applications, their efficient execution in distributed environments becomes a crucial issue. This work proposes a novel approach for distributing sparse matrix arithmetic operations on computer clusters aiming at speeding-up the processing of high-dimensional matrices. The approach focuses on how to split such operations into independent parallel tasks by considering the intrinsic characteristics that distinguish each type of operation and the particular matrices involved. The approach was applied to the most commonly used arithmetic operations between matrices. The performance of the presented approach was evaluated considering a high-dimensional text feature selection approach and two real-world datasets. Experimental evaluation showed that the proposed approach helped to significantly reduce the computing times of big-scale matrix operations, when compared to serial and multi-thread implementations as well as several linear algebra software libraries.     

HWOA: an intelligent hybrid whale optimization algorithm for multi-objective task selection strategy in edge cloud computing system

Edge computing is a popular computing modality that works by placing computing resources as close as possible to the sensor data to relieve the burden of network bandwidth and data centers in cloud computing. However, as the volume of data and the scale of tasks processed by edge terminals continue to increase, the problem of how to optimize task selection based on execution time with limited computing resources becomes a pressing one. To this end, a hybrid whale optimization algorithm (HWOA) is proposed for multi-objective edge computing task selection. In addition to the execution time of the task, economic profits are also considered to optimize task selection. Specifically, a fuzzy function is designed to address the uncertainty of task’s economic profits and execution time. Five interactive constraints among tasks are presented and formulated to improve the performance of task selection. Furthermore, some improved strategies are designed to solve the problem that the whale optimization algorithm (WOA) is subject to local optima entrapment. Finally, an extensive experimental assessment of synthetic datasets is implemented to evaluate the multi-objective optimization performance. Compared with the traditional WOA, the diversity metric (Δ-spread), the hypervolume (HV) and other evaluation metrics are significantly improved. The experiment results also indicate the proposed approach achieves remarkable performance compared with other competitive methods.

     

鲸鱼优化算法研究综述

鲸鱼优化算法是一种受座头鲸的狩猎行为启发所提出的新型群体智能优化算法,其性能远超传统算法。为了跟踪最新研究成果、促进群体智能优化算法领域的研究,首先介绍了鲸鱼优化算法的基本原理,简述了其实现步骤,并对比分析了其性能特点。针对其收敛速度慢、收敛精度低等缺点,总结分析了融入其他智能优化算法、Lévy飞行策略、混沌策略等改进方式及其优势与局限;接着阐述了其在组合优化、图像分割、数据预测等方面的应用及取得的效果。最后,对未来发展趋势进行总结与展望。     

混合策略改进鲸鱼优化算法

针对标准鲸鱼优化算法（whale optimization algorithm,WOA）易陷入局部最优解、收敛精度低、收敛速度慢等问题,提出一种利用混合策略改进的鲸鱼优化算法（multi-strategy improved whale optimization algorithm,MSIWOA）。首先采取精英反向策略初始化种群,提高初始种群质量;其次,采取卡方分布的逆累积分布函数更新收敛因子以实现全局探索和局部开发的平衡;然后利用改进氏族拓扑结构强化种群的全局探索能力,并提高算法收敛速度;最后采取Circle映射产生混沌解,结合贪婪策略保留较优解,以帮助种群跳出局部最优解。通过对10个基准测试函数以及CEC2019测试函数进行仿真实验,结果表明,MSIWOA在收敛精度和收敛速度上均有较明显的提升。     

A novel bacterial foraging optimization algorithm for feature selection

Bacterial foraging optimization (BFO) algorithm is a new swarming intelligent method, which has a satisfactory performance in solving the continuous optimization problem based on the chemotaxis, swarming, reproduction and elimination-dispersal steps. However, BFO algorithm is rarely used to deal with feature selection problem. In this paper, we propose two novel BFO algorithms, which are named as adaptive chemotaxis bacterial foraging optimization algorithm (ACBFO) and improved swarming and elimination-dispersal bacterial foraging optimization algorithm (ISEDBFO) respectively. Two improvements are presented in ACBFO. On the one hand, in order to solve the discrete problem, data structure of each bacterium is redefined to establish the mapping relationship between the bacterium and the feature subset. On the other hand, an adaptive method for evaluating the importance of features is designed. Therefore the primary features in feature subset are preserved. ISEDBFO is proposed based on ACBFO. ISEDBFO algorithm also includes two modifications. First, with the aim of describing the nature of cell to cell attraction-repulsion relationship more accurately, swarming representation is improved by means of introducing the hyperbolic tangent function. Second, in order to retain the primary features of eliminated bacteria, roulette technique is applied to the elimination-dispersal phase.In this study, ACBFO and ISEDBFO are tested with 10 public data sets of UCI. The performance of the proposed methods is compared with particle swarm optimization based, genetic algorithm based, simulated annealing based, ant lion optimization based, binary bat algorithm based and cuckoo search based approaches. The experimental results demonstrate that the average classification accuracy of the proposed algorithms is nearly 3 percentage points higher than other tested methods. Furthermore, the improved algorithms reduce the length of the feature subset by almost 3 in comparison to other methods. In addition, the modified methods achieve excellent performance on wilcoxon signed-rank test and sensitivity-specificity test. In conclusion, the novel BFO algorithms can provide important support for the expert and intelligent systems.     

鲸鱼优化算法的全局收敛性分析及参数选择研究

鲸鱼优化算法是一种设计新颖的智能优化算法,近年来己广泛应用于各种工程优化问题.但是关于鲸鱼优化算法的收敛性尚未明确,而且缺乏对算法中合理参数选择范围的理论分析.本文利用随机过程理论中的马尔科夫链分析了鲸鱼优化算法的全局收敛性,证明了算法中的收缩包围机制是决定鲸鱼优化算法是否收敛的关键因素.进一步建立了鲸鱼优化算法收缩包...     

高维数据挖掘中特征选择的稳健方法

针对高维数据的特点,即数据中变量个数往往大于样本观测数目,并且数据往往具有异质性特点,基于众数回归分析和变量选择降维技术,提出了一种稳健有效的特征选择方法,利用局部二次逼近算法(LQA)和最大期望(EM)算法,给出估计算法和最优调节参数的选取方法。通过实验的模拟数据分析表明,所提出的特征提取选择方法整体优于基于最小二乘和中位数的正则化估计方法,特别当误差是非正态分布时,与已有方法相比具有较高的预测能力和稳健性。