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Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms

International Journal of Mining Science and Technology 2023年第8期33卷 1019-1036页

作者： Chuanqi Li Jian Zhou Kun Du Daniel Dias School of Resources and Safety Engineering Central South UniversityChangsha 410083China Laboratory 3SR CNRS UMR 5521Grenoble Alpes UniversityGrenoble 38000France

Hard rock pillar is one of the important structures in engineering design and excavation in underground *** and convenient prediction of pillar stability is of great significance for underground space *** paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar *** integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM *** parameters including pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input *** global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models’*** results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local ***,the performance of the SSASVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published *** validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.

关键词： Underground pillar stability Hard rock Support vector machine metaheuristic algorithms

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A Hybrid Model Using Bio-Inspired metaheuristic algorithms for Network Intrusion Detection System

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Computers, Materials & Continua 2021年第7期68卷 409-429页

作者： Omar Almomani Department of Computer Network and Information Systems The World Islamic Sciences and Education UniversityAmman11947Jordan

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 *** features selection approach plays an important role in constructing effective network *** bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more ***,this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic *** proposed model has two objectives;The first one is to reduce the number of selected features for Network *** objective was met through the hybridization of bioinspired metaheuristic algorithms with each other in a hybrid *** algorithms used in this paper are particle swarm optimization(PSO),multiverse 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 *** objective was met through employing the support vector machine(SVM),C4.5(J48)decision tree,and random forest(RF)***-NB15 dataset used for assessing the effectiveness of the proposed hybrid ***-NB15 dataset has nine attacks *** generic attack is the highest among ***,the proposed model aims to identify generic *** data showed that J48 is the best classifier compared to SVM and RF for the time needed to build the *** 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.

关键词： IDS metaheuristic algorithms PSO MVO GWO MFO WOA FFA BAT SVM J48 RF UNSW-NB15 dataset

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A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms

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Information Processing in Agriculture 2018年第1期5卷 162-172页

作者： Sajad Sabzi Yousef Abbaspour-Gilandeh Ginés García-Mateos Department of Biosystems Engineering College of AgricultureUniversity of Mohaghegh ArdabiliArdabilIran Computer Science and Systems Department University of Murcia30100 EspinardoMurciaSpain

Accurate classification of fruit varieties in processing factories and during post-harvesting applications is a challenge that has been widely *** paper presents a novel approach to automatic fruit identification applied to three common varieties of oranges(Citrus sinensis L.),namely Bam,Payvandi and Thomson.A total of 300 color images were used for the experiments,100 samples for each orange variety,which are publicly *** segmentation,263 parameters,including texture,color and shape features,were extracted from each sample using image *** them,the 6 most effective features were automatically selected by using a hybrid approach consisting of an artificial neural network and particle swarm optimization algorithm(ANN-PSO).Then,three different classifiers were applied and compared:hybrid artificial neural network–artificial bee colony(ANN-ABC);hybrid artificial neural network–harmony search(ANN-HS);and k-nearest neighbors(kNN).The experimental results show that the hybrid approaches outperform the results of *** average correct classification rate of ANN-HS was 94.28%,while ANN-ABS achieved 96.70%accuracy with the available data,contrasting with the 70.9%baseline accuracy of ***,this new proposed methodology provides a fast and accurate way to classify multiple fruits varieties,which can be easily implemented in processing *** main contribution of this work is that the method can be directly adapted to other use cases,since the selection of the optimal features and the configuration of the neural network are performed automatically using metaheuristic algorithms.

关键词： Computer vision Fruits classification Hybrid neural networks Image processing metaheuristic algorithms

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2-D DOA Estimation in a Cuboid Array Based on metaheuristic algorithms and Maximum Likelihood

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International Journal of Communications, Network and System Sciences 2020年第8期13卷 121-137页

作者： Gilberto Lopes Filho Ana Cláudia Barbosa Rezende Lucas Fiorini Cruz Flávio Henrique Teles Vieira Rodrigo Pinto Lemos Universidade Federal de Goiás (UFG) Goiánia Brazil

This paper proposes to apply the genetic algorithm and the firefly algorithm to enhance the estimation of the direction of arrival (DOA) angle of electromagnetic signals of a smart antenna array. This estimation is essential for beamforming, where the antenna array radiating pattern is steered to provide faster and reliable data transmission with increased coverage. This work proposes using metaheuristics to improve a maximum likelihood DOA estimator for an antenna array arranged in a uniform cuboidal geometry. The DOA estimation performance of the proposed algorithm was compared to that of MUSIC on different two dimensions scenarios. The metaheuristic algorithms present better performance than the well-known MUSIC algorithm.

关键词： metaheuristic algorithms Genetic Algorithm Firefly Algorithm DOA Estimation Maximum Likelihood

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metaheuristic algorithms for groundwater model parameter inversion:Advances and prospects

Deep Resources Engineering

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Deep Resources Engineering 2024年第2期1卷 101-108页

作者： Junjun Chen Zhenxue Dai National and Local Joint Engineering Laboratory of Internet Application Technology on Mine China University of Mining and TechnologyXuzhou 221116China College of Construction Engineering Jilin UniversityChangchun 130026China

Groundwater inverse modeling is a vital technique for estimating unmeasurable model parameters and enhancing numerical simulation *** paper comprehensively reviews the current advances and future prospects of metaheuristic algorithm-based groundwater model parameter ***,the simulation-optimization parameter estimation framework is introduced,which involves the integration of simulation models with metaheuristic *** subsequent sections explore the fundamental principles of four widely employed metaheuristic algorithms-genetic algorithm(GA),particle swarm optimization(PSO),simulated annealing(SA),and differential evolution(DE)-highlighting their recent applications in water resources research and related ***,a solute transport model is designed to illustrate how to apply and evaluate these four optimization algorithms in addressing challenges related to model parameter ***,three noteworthy directions are presented to address the common challenges among current studies,including balancing the diverse exploration and centralized exploitation within metaheuristic algorithms,local approxi-mate error of the surrogate model,and the curse of dimensionality in spatial variational heterogeneous *** summary,this review paper provides theoretical insights and practical guidance for further advancements in groundwater inverse modeling studies.

关键词： Groundwater Inverse modeling metaheuristic algorithms Genetic algorithm Particle swarm optimization Simulated annealing Differential evolution

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Advances in Manta Ray Foraging Optimization:A Comprehensive Survey

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Journal of Bionic Engineering 2024年第2期21卷 953-990页

作者： Farhad Soleimanian Gharehchopogh Shafi Ghafouri Mohammad Namazi Bahman Arasteh Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmiaIran Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabrizIran Department of Computer Engineering Maybod BranchIslamic Azad UniversityMaybodIran Department of Software Engineering Faculty of Engineering and Natural ScienceIstinye UniversityIstanbulTurkey

This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic *** in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from manta rays’unique foraging behaviors—specifically cyclone,chain,and somersault *** biologically inspired strategies allow for effective solutions to intricate physical *** its potent exploitation and exploration capabilities,MRFO has emerged as a promising solution for complex optimization *** utility and benefits have found traction in numerous academic *** its inception in 2020,a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE,Wiley,Elsevier,Springer,MDPI,Hindawi,and Taylor&Francis,as well as at international conference *** paper consolidates the available literature on MRFO applications,covering various adaptations like hybridized,improved,and other MRFO variants,alongside optimization *** trends indicate that 12%,31%,8%,and 49%of MRFO studies are distributed across these four categories respectively.

关键词： Manta ray foraging optimization metaheuristic algorithms Hybridization Improved Optimization

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Optimal design of a scaled-up PRO system using swarm intelligence approach

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Science China(Information Sciences) 2021年第12期64卷 175-188页

作者： Yingxue CHEN Zhongke SHI Bin XU Mohammad Hasan SHAHEED School of Power and Energy Northwestern Ploytechnical University School of Automatic Northwestern Polytechnical University School of Engineering and Materials Science Queen Mary University of London

In this study, the pressure-retarded osmosis(PRO) process is optimized using Harris hawks optimization(HHO)-based maximum power point tracking(MPPT) technology. To make the practical implementation of salinity-gradient-based energy harvesting using PRO feasible, MPPT is envisaged to play a substantial role. Therefore, this study focuses on the development of a novel MPPT controller using swarm intelligence. The HHO algorithm is the latest approach that mimics the unique chasing strategy of Harris hawks in nature. To test the cost effectiveness of the proposed method, two case studies with various operational scenarios are presented. Compared with the performance of selected well-known and recent approaches, such as perturb & observe, incremental mass resistance, and whale optimization algorithm techniques, that of the proposed metaheuristic-based MPPT technique is found to be highly *** also show that the proposed algorithm can overcome other methods’ limitations, such as low tracking efficiency; low robustness when encountered in various operational conditions, including temperature and salinity; and steady-state oscillations. Furthermore, the proposed MPPT strategy is suitable for use in other fields of renewable energy harvesting.

关键词： maximum power point tracking (MPPT) swarm intelligence metaheuristic algorithms pressure-retarded osmosis (PRO) Harris hawks optimization (HHO)

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Multi-strategies Boosted Mutative Crow Search Algorithm for Global Tasks:Cases of Continuous and Discrete Optimization

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Journal of Bionic Engineering 2022年第6期19卷 1830-1849页

作者： Weifeng Shan Hanyu Hu Zhennao Cai Huiling Chen Haijun Liu Maofa Wang Yuntian Teng School of Emergency Management Institute of Disaster PreventionLangfang065201China Institute of Geophysics China Earthquake AdministrationBeijing100081China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China

Crow Search Algorithm(CSA)is a swarm-based single-objective optimizer proposed in recent years,which tried to inspire the behavior of crows that hide foods in different locations and retrieve them when *** original version of the CSA has simple parameters and moderate ***,it often tends to converge slowly or get stuck in a locally optimal region due to a missed harmonizing strategy during the exploitation and exploration ***,strategies of mutation and crisscross are combined into CSA(CCMSCSA)in this paper to improve the performance and provide an efficient optimizer for various optimization *** verify the superiority of CCMSCSA,a set of comparisons has been performed reasonably with some well-established metaheuristics and advanced metaheuristics on 15 benchmark *** experimental results expose and verify that the proposed CCMSCSA has meaningfully improved the convergence speed and the ability to jump out of the local *** addition,the scalability of CCMSCSA is analyzed,and the algorithm is applied to several engineering problems in a constrained space and feature selection *** results show that the scalability of CCMSCSA has been significantly improved and can find better solutions than its competitors when dealing with combinatorial optimization *** proposed CCMSCSA performs well in almost all experimental ***,we hope the researchers can see it as an effective method for solving constrained and unconstrained optimization problems.

关键词： Crow search algorithm Feature selection Global optimization metaheuristic algorithms Engineering problems Bionic algorithm

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Estimation of optimum design of structural systems via machine learning

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Frontiers of Structural and Civil Engineering 2021年第6期15卷 1441-1452页

作者： Gebrail BEKDAŞ Melda YÜCEL Sinan Melih NIGDELI Department of Civil Engineering Istanbul University-CerrahpaşaIstanbul 34320Turkey

Three different structural engineering designs were investigated to determine optimum design variables,and then to estimate design parameters and the main objective function of designs directly,speedily,and *** different optimization operations were carried out:One used the harmony search(HS)algorithm,combining different ranges of both HS parameters and iteration with population *** other used an estimation application that was done via artificial neural networks(ANN)to find out the estimated values of *** explore the estimation success of ANN models,different test cases were proposed for the three structural *** of the study suggest that ANN estimation for structures is an effective,successful,and speedy tool to forecast and determine the real optimum results for any design model.

关键词： optimization metaheuristic algorithms harmony search structural designs machine learning artificial neural networks

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An Effective Hybridization of Quantum-based Avian Navigation and Bonobo Optimizers to Solve Numerical and Mechanical Engineering Problems

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Journal of Bionic Engineering 2023年第3期20卷 1361-1385页

作者： Mohammad H.Nadimi-Shahraki Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran

Hybrid metaheuristic algorithms play a prominent role in improving algorithms' searchability by combining each algorithm's advantages and minimizing any substantial shortcomings. The Quantum-based Avian Navigation Optimizer Algorithm (QANA) is a recent metaheuristic algorithm inspired by the navigation behavior of migratory birds. Different experimental results show that QANA is a competitive and applicable algorithm in different optimization fields. However, it suffers from shortcomings such as low solution quality and premature convergence when tackling some complex problems. Therefore, instead of proposing a new algorithm to solve these weaknesses, we use the advantages of the bonobo optimizer to improve global search capability and mitigate premature convergence of the original QANA. The effectiveness of the proposed Hybrid Quantum-based Avian Navigation Optimizer Algorithm (HQANA) is assessed on 29 test functions of the CEC 2018 benchmark test suite with different dimensions, 30, 50, and 100. The results are then statistically investigated by the Friedman test and compared with the results of eight well-known optimization algorithms, including PSO, KH, GWO, WOA, CSA, HOA, BO, and QANA. Ultimately, five constrained engineering optimization problems from the latest test suite, CEC 2020 are used to assess the applicability of HQANA to solve complex real-world engineering optimization problems. The experimental and statistical findings prove that the proposed HQANA algorithm is superior to the comparative algorithms.

关键词： Optimization metaheuristic algorithms Evolutionary algorithm Quantum-based avian navigation optimizer algorithm Engineering optimization problems,Bionic algorithm

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