A fuzzy hierarchical operator in the grey wolf optimizer algorithm

The main goal of this paper is to study the performance of the Grey Wolf Optimizer (GWO) algorithm when a new hierarchical operator is introduced in the algorithm. This new operator is basically a hierarchical transformation that is inspired in the hierarchical social pyramid of the grey wolf. This proposed operator is applied to the simulation of the hunting process in the algorithm and has 5 variants that are explained in more detail in this paper (centroid, weighted, based on the fitness and two variants using fuzzy logic). Notably the variants having the greatest impact in the GWO performance are based on the use of fuzzy logic. We also present the motivation and results of experiments, as well as the benchmark functions that were used for the tests that are presented. In addition we are presenting a comparison among all methods for 30, 64 and 128 dimensions and we conclude that the performance of the Hierarchical GWO algorithm is better when using a fuzzy variant of the hierarchical operator.     

An Efficient Hybrid Approach to Solve Bi-objective Multi-area Dynamic Economic Emission Dispatch Problem

Abstract

Single period economic dispatch cannot handle the intertemporal constraints in multi-period environment. To cope with this issue, the extension of economic dispatch over multiple time intervals (i.e., dynamic economic dispatch) has been introduced that considers the intertemporal constraints between different time intervals. Another issue is determining the most economical generation dispatch that could supply the area demand without violating the tie-line capacity, which cannot be solved by conventional economic dispatch problems. However, this study shows that the most economic schedule of power generation cannot satisfy echo-system expectation; therefore, making a compromise between fuel cost and environmental issues, a hot-button subject in industrialized nations, seems to be crucial. To reach the goals a bi-objective multi-area dynamic economic dispatch approach, which can handle intertemporal and multi-area constraints concurrently, is proposed to assist power system operators more and more. Finally, a hybrid algorithm, namely gray wolf optimizer-particle swarm optimization is introduced to solve the proposed problem and also a set of benchmark problems. By implementing the proposed approach on two small (10-unit, three areas) and large (40-unit, four areas) scale test systems, about 3.1% and 3.3% improvement in generation cost is obtained, respectively compare to the best reported results in the literature.     

基于VMD与GWO优化SVM的轴承故障诊断

针对传统振动信号特征提取方法与支持向量机(SVM)分类方法的缺陷,提出一种基于变分模态分解(VMD)故障特征提取方法与灰狼优化器(GWO)优化SVM的诊断模型。首先,将滚动轴承的原始振动信号采用VMD得到若干本征模态分量(IMF);其次,将IMF的多尺度加权排列熵作为特征向量并使用t-sne方法做降维处理;最后,使用GWO对SVM进行优化并对样本数据进行分类判别。实验结果表明,该方法相比于其他传统算法能够有效提高故障分类精度。     

基于GWO-SVM的下肢假肢穿戴者骑行相位识别

针对下肢假肢穿戴者骑行相位识别的问题,提出基于灰狼算法优化的支持向量机(GWO-SVM)分类模型. 建立下肢多源信息系统,采集膝关节、踝关节的加速度信号以及膝关节角度信号. 应用奇异值分解,对采集到的信号进行降噪处理. 在对信号进行降噪处理之后,为了避免单一信号不确定的影响,从数据冗余角度,选取各信号的特征点,开展归一化处理,组成多维特征向量,作为SVM分类模型的输入. 为了能够进一步提高分类精度,加强全局优化能力,利用GWO算法对核参数进行优化. 通过与PSO-SVM分类模型、GA-SVM分类模型对比表明,基于GWO优化的SVM分类模型对骑行相位的识别率为94%,高于其他方法优化的SVM分类模型.     

基于GWO-BP神经网络算法的WFGD系统在线优化

以浆液循环泵运行情况作为工况划分条件,通过提出的灰狼优化(GWO)-BP神经网络(GWO-BP神经网络)算法建立了针对湿法烟气脱硫(WFGD)系统多模态在线优化模型组,分析了机组负荷、入口SO₂质量浓度对出口SO2质量浓度变化量的影响,并利用某660 MW机组切换试验对该模型组性能进行验证。结果表明:随着机组负荷和入口SO₂质量浓度增大,切换后出口SO₂质量浓度变化量增大,该模型组具有较好的预测精度和泛化能力。     

A Hybrid Model Using Bio-Inspired Metaheuristic Algorithms for Network Intrusion Detection System

Network Intrusion Detection System (IDS) aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls. The features selection approach plays an important role in constructing effective network IDS. Various bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more accuracy. Therefore, this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic attack. The proposed model has two objectives; The first one is to reduce the number of selected features for Network IDS. This objective was met through the hybridization of bio-inspired metaheuristic algorithms with each other in a hybrid model. The algorithms used in this paper are particle swarm optimization (PSO), multi-verse optimizer (MVO), grey wolf optimizer (GWO), moth-flame optimization (MFO), whale optimization algorithm (WOA), firefly algorithm (FFA), and bat algorithm (BAT). The second objective is to detect the generic attack using machine learning classifiers. This objective was met through employing the support vector machine (SVM), C4.5 (J48) decision tree, and random forest (RF) classifiers. UNSW-NB15 dataset used for assessing the effectiveness of the proposed hybrid model. UNSW-NB15 dataset has nine attacks type. The generic attack is the highest among them. Therefore, the proposed model aims to identify generic attacks. My data showed that J48 is the best classifier compared to SVM and RF for the time needed to build the model. In terms of features reduction for the classification, my data show that the MFO-WOA and FFA-GWO models reduce the features to 15 features with close accuracy, sensitivity and F-measure of all features, whereas MVO-BAT model reduces features to 24 features with the same accuracy, sensitivity and F-measure of all features for all classifiers.     

A*初始化的变异灰狼优化的无人机路径规划

无人机（unmanned aerial vehicle,UAV）路径规划问题是无人机任务规划系统的重要组成部分,需要在一个存在威胁区的搜索空间中获得最优路径。为解决灰狼优化算法存在收敛速度慢、容易陷入局部最优等问题,提出了一种基于A*初始化的变异灰狼优化算法。该算法首先将模型离散化,进而使用A*算法进行头狼的初始化,使后续算法有一个较优的起点,随后通过简化后的灰狼优化算法在连续模型上构建和更新种群,在迭代过程中,通过新提出的一种新型修正变异算子优化种群。利用三次B样条平滑后的无人机航迹,符合无人机的性能要求。经实验验证,算法在代价收敛速度、求取的最终路径以及算法稳定性方面均优于粒子群算法（particle swarm optimization,PSO）、灰狼优化算法（gray wolf optimizer,GWO）、共生生物搜索算法（symbiotic organisms search,SOS）算法,在解决无人机路径规划问题上具有较高的应用价值。     

重选精英个体的非线性收敛灰狼优化算法

针对灰狼优化算法（GWO）存在的求解精度较低、后期收敛速度较慢、易陷入局部最优的缺点,提出一种改进灰狼优化算法（EGWO）。该算法引进两种改进策略：用以平衡算法全局搜索性和局部开发性的非线性收敛因子调整策略和用以降低陷入局部最优风险的精英个体重选策略。通过在9个基准测试函数上的实验与标准GWO算法,以及文献提出的5种改进灰狼算法和4种其他算法进行对比,从算法寻优的精确性和鲁棒性两个方面验证两种算法改进策略的有效性。实验结果表明,两种改进策略都能提升算法性能,综合使用两种策略的EGWO在收敛速度和求解精度都明显优于其他比较算法。     

求解不相关并行机调度问题的十进制多目标灰狼算法

针对不相关并行机调度问题,面向降低能源消耗和减少完工时间的目标,提出一种更高效的基于十进制整数编码的多目标灰狼算法.求解时,采用将资源配置与作业排序相结合的十进制整数编码方式,设计了针对多目标离散调度问题的两阶段位置更新机制.同时引入了N S GA-Ⅱ的精英保留策略,提高了算法的寻优能力,应用最大迭代次数停止准则结束循环并保留最优解.最后,通过数值实验与有代表性的前沿算法进行仿真对比,以验证所提算法的可行性与有效性.     

一种结合灰狼算法的粒子群优化算法

针对粒子群算法有陷入局部最优的缺点,提出一种基于灰狼算法的粒子群优化算法.首先,根据自然界中优胜劣汰的生存法则,对每次迭代种群中的最差粒子进行进化,其次,由于粒子群算法中整个种群中的最优粒子有很强的引导能力,对最优粒子进行扰动,增大寻找全局最优的可能性;最后,结合灰狼优化算法,引导粒子群包围式进行搜索,增强全局搜索能力;将改进的粒子群算法与标准粒子群算法在9个测试函数上进行了寻优精度和收敛速度的对比,结果证明改进粒子群算法(PSO_GWO)在收敛速度和寻优精度上均优于粒子群算法(PSO).