Optimizing Grey Wolf Optimization: A Novel Agents’ Positions Updating Technique for Enhanced Efficiency and Performance

Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.

VGWO: Variant Grey Wolf Optimizer with High Accuracy and Low Time Complexity

The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf hunting.Along with its advantages of simple principle and few parameters setting,GWO bears drawbacks such as low solution accuracy and slow convergence speed.A few recent advanced GWOs are proposed to try to overcome these disadvantages.However,they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early convergence.To solve the abovementioned issues,a high-accuracy variable grey wolf optimizer(VGWO)with low time complexity is proposed in this study.VGWO first uses the symmetrical wolf strategy to generate an initial population of individuals to lay the foundation for the global seek of the algorithm,and then inspired by the simulated annealing algorithm and the differential evolution algorithm,a mutation operation for generating a new mutant individual is performed on three wolves which are randomly selected in the current wolf individuals while after each iteration.A vectorized Manhattan distance calculation method is specifically designed to evaluate the probability of selecting the mutant individual based on its status in the current wolf population for the purpose of dynamically balancing global search and fast convergence capability of VGWO.A series of experiments are conducted on 19 benchmark functions from CEC2014 and CEC2020 and three real-world engineering cases.For 19 benchmark functions,VGWO’s optimization results place first in 80%of comparisons to the state-of-art GWOs and the CEC2020 competition winner.A further evaluation based on the Friedman test,VGWO also outperforms all other algorithms statistically in terms of robustness with a better average ranking value.

Hybridized Intelligent Neural Network Optimization Model for Forecasting Prices of Rubber in Malaysia

Rubber producers,consumers,traders,and those who are involved in the rubber industry face major risks of rubber price fluctuations.As a result,decision-makers are required to make an accurate estimation of the price of rubber.This paper aims to propose hybrid intelligent models,which can be utilized to forecast the price of rubber in Malaysia by employing monthly Malaysia’s rubber pricing data,spanning from January 2016 to March 2021.The projected hybrid model consists of different algorithms with the symbolic Radial Basis Functions Neural Network k-Satisfiability Logic Mining(RBFNN-kSAT).These algorithms,including Grey Wolf Optimization Algorithm,Artificial Bee Colony Algorithm,and Particle Swarm Optimization Algorithm were utilized in the forecasting data analysis.Several factors,which affect the monthly price of rubber,such as rubber production,total exports of rubber,total imports of rubber,stocks of rubber,currency exchange rate,and crude oil prices were also considered in the analysis.To evaluate the results of the introduced model,a comparison has been conducted for each model to identify the most optimum model for forecasting the price of rubber.The findings showed that GWO with RBFNN-kSAT represents the most accurate and efficient model compared with ABC with RBFNNkSAT and PSO with RBFNN-kSAT in forecasting the price of rubber.The GWO with RBFNN-kSAT obtained the greatest average accuracy(92%),with a better correlation coefficient R=0.983871 than ABC with RBFNN-kSAT and PSO with RBFNN-kSAT.Furthermore,the empirical results of this study provided several directions for policymakers to make the right decision in terms of devising proper measures in the industry to address frequent price changes so that the Malaysian rubber industry maintains dominance in the international markets.

Grey Wolf Optimizer to Real Power Dispatch with Non-Linear Constraints

A new and efficient Grey Wolf Optimization(GWO)algorithm is implemented to solve real power economic dispatch(RPED)problems in this paper.The nonlinear RPED problem is one the most important and fundamental optimization problem which reduces the total cost in generating real power without violating the constraints.Conventional methods can solve the ELD problem with good solution quality with assumptions assigned to fuel cost curves without which these methods lead to suboptimal or infeasible solutions.The behavior of grey wolves which is mimicked in the GWO algorithm are leadership hierarchy and hunting mechanism.The leadership hierarchy is simulated using four types of grey wolves.In addition,searching,encircling and attacking of prey are the social behaviors implemented in the hunting mechanism.The GWO algorithm has been applied to solve convex RPED problems considering the all possible constraints.The results obtained from GWO algorithm are compared with other state-ofthe-art algorithms available in the recent literatures.It is found that the GWO algorithm is able to provide better solution quality in terms of cost,convergence and robustness for the considered ELD problems.

Biological Network Modeling Based on Hill Function and Hybrid Evolutionary Algorithm

Gene regulatory network inference helps understand the regulatory mechanism among genes, predict the functions of unknown genes, comprehend the pathogenesis of disease and speed up drug development. In this paper, a Hill function-based ordinary differential equation (ODE) model is proposed to infer gene regulatory network (GRN). A hybrid evolutionary algorithm based on binary grey wolf optimization (BGWO) and grey wolf optimization (GWO) is proposed to identify the structure and parameters of the Hill function-based model. In order to restrict the search space and eliminate the redundant regulatory relationships, L1 regularizer was added to the fitness function. SOS repair network was used to test the proposed method. The experimental results show that this method can infer gene regulatory network more accurately than state of the art methods.

Smart Fraud Detection in E-Transactions Using Synthetic Minority Oversampling and Binary Harris Hawks Optimization

Fraud Transactions are haunting the economy of many individuals with several factors across the globe.This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital transactions.This research proposes a novel methodology through three stages.Firstly,Synthetic Minority Oversampling Technique(SMOTE)is applied to get balanced data.Secondly,SMOTE is fed to the nature-inspired Meta Heuristic(MH)algorithm,namely Binary Harris Hawks Optimization(BinHHO),Binary Aquila Optimization(BAO),and Binary Grey Wolf Optimization(BGWO),for feature selection.BinHHO has performed well when compared with the other two.Thirdly,features from BinHHO are fed to the supervised learning algorithms to classify the transactions such as fraud and non-fraud.The efficiency of BinHHO is analyzed with other popular MH algorithms.The BinHHO has achieved the highest accuracy of 99.95%and demonstrates amore significant positive effect on the performance of the proposed model.

含不相关机的多目标混合流水车间调度

考虑不相关机和传送等因素的多阶段混合流水车间问题,以最小化最大完工时间和总能耗为优化目标建立整数规划模型。针对该问题,提出一种多目标离散灰狼优化算法来求解。设计基于机器分配码和速度选择码的编码方式和基于最短处理时间原则的解码方案;采用反向学习策略改进初始灰狼种群质量;将基于多点变异的自走模式和基于均匀两点交叉与多点交叉的跟随模式结合构成搜索模式以协调开发和搜索能力;引入精英保留策略确保优良个体不丢失。通过一系列的仿真实验验证了该算法的有效性。

局部阴影下基于GWO-P&O混合算法的光伏最大功率点跟踪

针对局部遮阴环境下传统灰狼优化(Gray wolf optimization,GWO)算法在跟踪最大功率点时P-U特性曲线出现多峰值、后期收敛速度慢、稳态精度低等问题,结合灰狼优化算法和扰动观察法(Perturbation and observation,P&O)各自的优势,提出了基于GWO-P&O的混合优化最大功率点跟踪(Maximum power point tracking,MPPT)算法。首先,采用灰狼优化算法逐渐向光伏的全局最大功率点靠近。其次,在灰狼优化算法收敛后期引入P&O法,既保持了灰狼优化算法较高的稳态精度,又能以较快速度寻找到局部最大功率点。最后,在不同环境工况下,将所提出的GWO-P&O方法与传统GWO算法进行对比。结果表明,改进的GWO-P&O算法在保证良好稳态性能的同时,一定程度上提高了GWO算法后期跟踪最大功率时的收敛速度。

A Hybrid of Grey Wolf Optimization and Genetic Algorithm for Optimization of Hybrid Wind and Solar Renewable Energy System

In this paper,a hybrid of grey wolf optimization(GWO)and genetic algorithm(GA)has been implemented to minimize the annual cost of hybrid of wind and solar renewable energy system.It was named as hybrid of grey wolf optimization and genetic algorithm(HGWOGA).HGWOGA was applied to this hybrid problem through three procedures.First,the balance between the exploration and the exploitation process was done by grey wolf optimizer algorithm.Then,we divided the population into subpopulation and used the arithmetical crossover operator to utilize the dimension reduction and the population partitioning processes.At last,mutation operator was applied in the whole population in order to refrain from the premature convergence and trapping in local minima.MATLAB code was designed to implement the proposed methodology.The result of this algorithm is compared with the results of iteration method,GWO,GA,artificial bee colony(ABC)and particle swarm optimization(PSO)techniques.The results obtained by this algorithm are better when compared with those mentioned in the text.

基于HGWO-MSVM的采煤机滚动轴承故障诊断方法

针对采煤机滚动轴承故障特征向量提取较困难、多分类效果不理想等问题,提出了基于HGWOMSVM的采煤机轴承故障诊断方法。对轴承故障信号进行小波降噪处理,利用经验模态分解算法对降噪后信号进行分解,并提取能量特征值,作为MSVM的训练集和测试集。采用MSVM进行故障状态识别,并用HGWO算法对MSVM的参数进行优化。试验结果表明,相比于GWO、GA和PSO优化MSVM模型,基于HGWO-MSVM的采煤机轴承故障诊断模型可明显提高故障识别精度和效率。

基于相似日聚类和IHGWO-WNN-AdaBoost模型的短期光伏功率预测

为进一步提升光伏输出功率短期预测的准确性和稳定性,提出一种基于相似日聚类的小波神经网络(wavelet neural network,WNN)和AdaBoost的混合预测模型。首先利用模糊C均值聚类(fuzzy C-means algorithm,FCM)算法将初始数据集按照不同的季节和天气类型进行划分;其次选用WNN作为改进AdaBoost算法的基学习器,构建WNN-AdaBoost模型,并使用改进混合灰狼优化(improved hybridizing grey wolf optimization,IHGWO)算法优化WNN的小波因子和权值;最后选用我国中部地区某光伏电站实采的输出功率数据进行算例分析,通过与其他模型的对比,验证了所提模型的预测效果。实验结果表明:在不同季节和天气类型下,所提模型均能得到较好的预测结果,在有效提升光伏短期输出功率预测精度的同时,兼备了较强的适应性和鲁棒性。

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Swarm-Based Extreme Learning Machine Models for Global Optimization

Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space complexity.In ELM,the hidden layer typically necessitates a huge number of nodes.Furthermore,there is no certainty that the arrangement of weights and biases within the hidden layer is optimal.To solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization techniques.This paper displays five proposed hybrid Algorithms“Salp Swarm Algorithm(SSA-ELM),Grasshopper Algorithm(GOA-ELM),Grey Wolf Algorithm(GWO-ELM),Whale optimizationAlgorithm(WOA-ELM)andMoth Flame Optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression data.The proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear period.In the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of ELM.The best weights and preferences were calculated by these algorithms for the hidden layer.Experimental results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression data.While in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.

Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

Intrusion detection is a serious and complex problem.Undoubtedly due to a large number of attacks around the world,the concept of intrusion detection has become very important.This research proposes a multilayer bioinspired feature selection model for intrusion detection using an optimized genetic algorithm.Furthermore,the proposed multilayer model consists of two layers(layers 1 and 2).At layer 1,three algorithms are used for the feature selection.The algorithms used are Particle Swarm Optimization(PSO),Grey Wolf Optimization(GWO),and Firefly Optimization Algorithm(FFA).At the end of layer 1,a priority value will be assigned for each feature set.At layer 2 of the proposed model,the Optimized Genetic Algorithm(GA)is used to select one feature set based on the priority value.Modifications are done on standard GA to perform optimization and to fit the proposed model.The Optimized GA is used in the training phase to assign a priority value for each feature set.Also,the priority values are categorized into three categories:high,medium,and low.Besides,the Optimized GA is used in the testing phase to select a feature set based on its priority.The feature set with a high priority will be given a high priority to be selected.At the end of phase 2,an update for feature set priority may occur based on the selected features priority and the calculated F-Measures.The proposed model can learn and modify feature sets priority,which will be reflected in selecting features.For evaluation purposes,two well-known datasets are used in these experiments.The first dataset is UNSW-NB15,the other dataset is the NSL-KDD.Several evaluation criteria are used,such as precision,recall,and F-Measure.The experiments in this research suggest that the proposed model has a powerful and promising mechanism for the intrusion detection system.

基于灰狼算术混合优化算法的类集成测试序列生成方法

集成测试是软件测试的重要环节,如何决定类的集成顺序是面向对象集成测试难解决的问题之一。已有研究成果证实了基于搜索的类集成测试序列生成方法的有效性,但存在收敛速度慢、寻优精度低的问题。灰狼优化算法(Grey Wolf Optimizer, GWO)中狼群易聚集在相近的区域,易早熟收敛。算术优化算法(Arithmetic Optimization Algorithm, AOA)是新近提出的元启发式优化算法,具有良好的随机性及分散性。为此,提出了一种灰狼优化算法和算术优化算法的混合优化算法(GWO-AOA)。GWO-AOA保留GWO的位置更新策略,选用群体领导层的中心个体替换AOA的引导个体,以平衡算法的全局探索和局部开发能力,进一步引入随机游动的精英变异机制,提高算法整体的寻优精度。实验结果表明,GWO-AOA相比同类方法能用较短的时间生成测试桩代价较低的类集成测试序列,收敛速度较快。

基于灰狼-鸟群算法的特征权重优化方法

针对特征权重难以准确量化的问题,提出一种基于灰狼优化(grey wolf optimizer, GWO)算法和鸟群算法(bird swarm algorithm, BSA)的混合算法,用于特征权重的寻优。首先,将Chebyshev映射、反向学习与精英策略用于混合算法的初始种群生成;其次,将改进后的GWO算法位置更新策略融入BSA的觅食行为中,得到一种新的局部搜索策略;然后,将BSA的警觉行为与飞行行为用作混合算法的全局搜索平衡策略,从而得到一种收敛的灰狼-鸟群算法(grey wolf and bird swarm algorithm, GWBSA),通过GWBSA的迭代寻优可获得各特征的权重值。利用标准测试函数和标准分类数据集进行了对比实验,与遗传算法、蚁狮算法等方法相比,GWBSA具有较快的收敛速度且不易陷入局部最优,可以提高模式分类问题的求解质量。

基于HPO-SVM的拖拉机柴油机故障诊断研究

[目的]针对传统机器学习在拖拉机柴油机故障诊断应用中的局限性,本研究提出一种HPO-SVM(hybrid population optimization-support vector machine)拖拉机柴油机故障诊断模型。[方法]采用SVM(support vector machine)作为故障诊断模型的基体,针对SVM优化问题,以PSO(particle swarm optimization)和GWO(grey wolf optimization)算法为基础提出了HPO(hybrid population optimization)算法对SVM的重要参数c、g进行优化;分析柴油机的故障机制,确定反映故障发生的数据信号;基于CAN(controller area network)总线和Arduino UNO-MCP 2551组合模块采集潍柴WP6型拖拉机柴油机传感器信号数据对HPO-SVM的性能进行测试,并将测试结果与SVM、PSO-SVM、GWO-SVM、GWOPSO-SVM和LWD-QPSO-SOMBP(linear weight decrease-quantum particle swarm optimization-self organizing maps back propagation)神经网络的测试结果进行对比。[结果]相比于其他4种SVM模型,HPO-SVM充分发挥了GWO算法和PSO算法在SVM参数寻优方面的优势,故障诊断准确率大幅度提升,相比于SVM,诊断总准确率由80%上升至100%,提高20%;HPO算法提高了单种群优化算法的寻优性能,相较于PSO算法,HPO算法最佳适应度由70提升至90,提高22.22%,达到最佳适应度时的迭代次数由105下降至27,下降74.29%;为避免偶然性,对5种SVM模型采取6次重复试验,试验结果表明,相较于其他4种模型HPO-SVM模型的性能更稳定,HPO-SVM的6次诊断总准确率均为100%;HPO-SVM采用SVM作为故障诊断模型,缓解优化算法的寻优压力,提高模型的效率,相比于LWD-QPSO-SOMBP神经网络,HPO-SVM模型的运行时间由45 s降低至15 s,下降66.67%。[结论]本文研究结果可为高效率拖拉机柴油机故障诊断提供参考。

蜂窝网络D2D通信资源分配研究

针对D2D通信复用蜂窝用户频谱可以提高系统吞吐量和频谱利用率但会产生严重的同频干扰的问题,设计了一种联合资源分配方案,该方案在保证系统用户通信质量的条件下最大化系统吞吐量,首先设计一种基于吞吐量最优化的模式选择方案,然后采用混合遗传算法对D2D用户分配信道,最后在已知信道分配向量的前提下通过混合灰狼优化算法对D2D用户进行功率优化。仿真结果表明,所提方案相对其他方案能够有效提高20%系统总吞吐量和降低90%蜂窝用户受到的干扰,还能提高收敛效果和运行速度。

融合DE-GWO与SVR的文化意象预测模型

为更客观准确的量化文化特征与意象间的关系,提出一种融合混合灰狼优化算法(DE-GWO)与支持向量回归(SVR)的文化意象预测模型。首先,构建以多组意象词汇为基础的响堂山石窟造像的文化特征的意象空间,并利用眼动追踪技术进行文化意象认知实验,获取被试生理认知数据并对其进行单因素方差分析,进而得到文化意象预测模型的眼动指标参数数据集;其次,引入基于DE算法的差分进化策略以弥补GWO搜索过程陷入停滞状态的问题;再次,利用改进后的GWO算法对SVR模型的参数C和g进行寻优;最后利用构建的DE-GWO-SVR模型实现对文化意象认知的预测。为了进一步证明所构建模型的泛化性,采用BP,ABC-SVR和DT等5种模型进行对比实验,结果表明该模型对于文化意象认知的预测效果更好。

基于GWO-ABC的混合算法研究

大多数种群优化算法面临的共同缺陷是全局搜索能力不足,易陷入局部最优解。文章基于灰狼优化算法和人工蜂群算法,引入混沌映射和OBL策略,提出了新型GWO-ABC混合优化算法。通过GWO-ABC算法优化了FOPID控制器的参数,仿真结果表明,该算法性能优于其它算法。