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.

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.

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.

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.

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.

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.

Discrete Improved Grey Wolf Optimizer for Community Detection

Detecting communities in real and complex networks is a highly contested topic in network analysis.Although many metaheuristic-based algorithms for community detection have been proposed,they still cannot effectively fulfill large-scale and real-world networks.Thus,this paper presents a new discrete version of the Improved Grey Wolf Optimizer(I-GWO)algorithm named DI-GWOCD for effectively detecting communities of different networks.In the proposed DI-GWOCD algorithm,I-GWO is first armed using a local search strategy to discover and improve nodes placed in improper communities and increase its ability to search for a better solution.Then a novel Binary Distance Vector(BDV)is introduced to calculate the wolves’distances and adapt I-GWO for solving the discrete community detection problem.The performance of the proposed DI-GWOCD was evaluated in terms of modularity,NMI,and the number of detected communities conducted by some well-known real-world network datasets.The experimental results were compared with the state-of-the-art algorithms and statistically analyzed using the Friedman and Wilcoxon tests.The comparison and the statistical analysis show that the proposed DI-GWOCD can detect the communities with higher quality than other comparative algorithms.

Medical Image Segmentation using PCNN based on Multi-feature Grey Wolf Optimizer Bionic Algorithm

Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image segmentation.Specifically,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image segmentation.The image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy region.In the stage of image segmentation,an improved PCNN model based on MFGWO is proposed,which can adaptively set the parameters of PCNN according to the features of the image.Two modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN algorithm.The experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.

Correction of array failure using grey wolf optimizer hybridized with an interior point algorithm

We design a grey wolf optimizer hybridized with an interior point algorithm to correct a faulty antenna array. If a single sensor fails, the radiation power pattern of the entire array is disturbed in terms of sidelobe level（SLL） and null depth level（NDL）, and nulls are damaged and shifted from their original locations. All these issues can be solved by designing a new fitness function to reduce the error between the preferred and expected radiation power patterns and the null limitations. The hybrid algorithm has been designed to control the array＇s faulty radiation power pattern. Antenna arrays composed of 21 sensors are used in an example simulation scenario. The MATLAB simulation results confirm the good performance of the proposed method, compared with the existing methods in terms of SLL and NDL.

Three Dimensional Optimum Node Localization in Dynamic Wireless Sensor Networks

Location information plays an important role in most of the applications in Wireless Sensor Network(WSN).Recently,many localization techniques have been proposed,while most of these deals with two Dimensional applications.Whereas,in Three Dimensional applications the task is complex and there are large variations in the altitude levels.In these 3D environments,the sensors are placed in mountains for tracking and deployed in air for monitoring pollution level.For such applications,2D localization models are not reliable.Due to this,the design of 3D localization systems in WSNs faces new challenges.In this paper,in order to find unknown nodes in Three-Dimensional environment,only single anchor node is used.In the simulation-based environment,the nodes with unknown locations are moving at middle&lower layers whereas the top layer is equipped with single anchor node.A novel soft computing technique namely Adaptive Plant Propagation Algorithm(APPA)is introduced to obtain the optimized locations of these mobile nodes.Thesemobile target nodes are heterogeneous and deployed in an anisotropic environment having an Irregularity(Degree of Irregularity(DOI))value set to 0.01.The simulation results present that proposed APPAalgorithm outperforms as tested among other meta-heuristic optimization techniques in terms of localization error,computational time,and the located sensor nodes.

Optimized Controller Gains Using Grey Wolf Algorithm for Grid Tied Solar Power Generation with Improved Dynamics and Power Quality

This study proposes a control algorithm based on synchronous reference frame theory with unit templates instead of a phase locked loop for grid-connected photovoltaic(PV)solar system,comprising solar PV panels,DC-DC converter,controller for maximum power point tracking,resistance capacitance ripple filter,insulated-gate bipolar transistor based controller,interfacing inductor,linear and nonlinear loads.The dynamic performance of the grid connected solar system depends on the effect operation of the control algorithm,comprising two proportional-integral controllers.These controllers estimate the reference solar-grid currents,which in turn generate pulses for the three-leg voltage source converter.The grey wolf optimization algorithm is used to optimize the controller gains of the proportional-integral controllers,resulting in excellent performance compared to that of existing optimization algorithms.The compensation for neutral current is provided by a star-delta transformer(non-isolated),and the proposed solar PV grid system provides zero voltage regulation and eliminates harmonics,in addition to load balancing.Maximum power extraction from the solar panel is achieved using the incremental conductance algorithm for the DC-DC converter supplying solar power to the DC bus capacitor,which in turn supplies this power to the grid with improved dynamics and quality.The solar system along with the control algorithm and controller is modeled using Simulink in Matlab 2019.

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.

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。

Comparative seismic design optimization of spatial steel dome structures through three recent metaheuristic algorithms

Steel dome structures,with their striking structural forms,take a place among the impressive and aesthetic load bearing systems featuring large internal spaces without internal columns.In this paper,the seismic design optimization of spatial steel dome structures is achieved through three recent metaheuristic algorithms that are water strider(WS),grey wolf(GW),and brain storm optimization(BSO).The structural elements of the domes are treated as design variables collected in member groups.The structural stress and stability limitations are enforced by ASD-AISC provisions.Also,the displacement restrictions are considered in design procedure.The metaheuristic algorithms are encoded in MATLAB interacting with SAP2000 for gathering structural reactions through open application programming interface(OAPI).The optimum spatial steel dome designs achieved by proposed WS,GW,and BSO algorithms are compared with respect to solution accuracy,convergence rates,and reliability,utilizing three real-size design examples for considering both the previously reported optimum design results obtained by classical metaheuristic algorithms and a gradient descent-based hyperband optimization(HBO)algorithm.

Advance Artificial Intelligence Technique for Designing Double T-Shaped Monopole Antenna

Machine learning(ML)has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls.ML is a massive area within artificial intelligence(AI)that focuses on obtaining valuable information out of data,explaining why ML has often been related to stats and data science.An advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture design.The algorithm is designed,depending on the hybrid between the Sine Cosine Algorithm(SCA)and the Grey Wolf Optimizer(GWO),to train neural networkbased Multilayer Perceptron(MLP).The proposed optimization algorithm is a practical,versatile,and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole antenna.The proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and T-Test.It offers the superiority and validation stability evaluation of the predicted results to verify the procedures’accuracy.

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.

Adaptive coordination control strategy of renewable energy sources,hydrogen production unit,and fuel celi for frequency regulation of a hybrid distributed power system

Owing to the significant number of hybrid generation systems(HGSs)containing various energy sources,coordina-tion between these sources plays a vital role in preserving frequency stability.In this paper,an adaptive coordination control strategy for renewable energy sources(RESs),an aqua electrolyzer(AE)for hydrogen production,and a fuel cell(FC)-based energy storage system(ESS)is proposed to enhance the frequency stability of an HGS.In the proposed system,the excess energy from RESs is used to power electrolysis via an AE for hydrogen energy storage in FCs.The proposed method is based on a proportional-integral(Pl)controller,which is optimally designed using a grey wolf optimization(GWO)algorithm to estimate the surplus energy from RESs(ie,a proportion of total power generation of RESs:Kn).The studied HGS contains various types of generation systems including a diesel generator,wind tur-bines,photovoltaic(PV)systems,AE with FCs,and ESSs(e.g.,battery and flywheel).The proposed method varies Kn with varying frequency deviation values to obtain the best benefits from RESs,while damping the frequency fluc-tuations.The proposed method is validated by considering different loading conditions and comparing with other existing studies that consider Kn as a constant value.The simulation results demonstrate that the proposed method,which changes Kn value and subsequently stores the power extracted from the RESs in hydrogen energy storage according to frequency deviation changes,performs better than those that use constant Kn.The statistical analysis for frequency deviation of HGS with the proposed method has the best values and achieves large improvements for minimum,maximum,difference between maximum and minimum,mean,and standard deviation compared to the existing method.

A hybrid machine learning model to estimate self-compacting concrete compressive strength

This study examined the feasibility of using the grey wolf optimizer(GWO)and artificial neural network(ANN)to predict the compressive strength(CS)of self-compacting concrete(SCC).The ANN-GWO model was created using 115 samples from different sources,taking into account nine key SCC factors.The validation of the proposed model was evaluated via six indices,including correlation coefficient(R),mean squared error,mean absolute error(MAE),IA,Slope,and mean absolute percentage error.In addition,the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence plots.The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS.Following that,an examination of the parameters impacting the CS of SCC was provided.