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Swarm-Based Extreme Learning Machine Models for Global Optimization

Computers, Materials & Continua 2022年第3期70卷 6339-6363页

作者： Mustafa Abdul Salam ahmad taher azar Rana Hussien Artificial Intelligence Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13518Egypt Scientific Computing Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13518Egypt

Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ***,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space *** ELM,the hidden layer typically necessitates a huge number of ***,there is no certainty that the arrangement of weights and biases within the hidden layer is *** solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization *** 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 *** proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear *** the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of *** best weights and preferences were calculated by these algorithms for the hidden *** 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 *** in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.

关键词： Extreme learning machine salp swarm optimization algorithm grasshopper optimization algorithm grey wolf optimization algorithm moth flame optimization algorithm bio-inspired optimization classification model and whale optimization algorithm

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Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search

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Computer Modeling in Engineering & Sciences 2020年第11期125卷 459-494页

作者： Soukaina Mjahed Khadija Bouzaachane ahmad taher azar Salah El Hadaj Said Raghay Faculty of Sciences and Technology Department of Applied Mathematics and Computer SciencesCadi Ayyad UniversityMarrakech40000Morocco Robotics and Internet-of-Things Lab(RIOTU) Prince Sultan UniversityRiyadh12435Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13511Egypt

This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data *** unsupervised detection goes in this paper analysis through 4 steps:(1)selection of the most informative features from the considered data;(2)definition of the number of clusters based on the elbow *** experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters;(3)proposition of a new approach for hybridization of both hard and fuzzy clustering tuned with Ant Lion Optimization(ALO);(4)comparison with some existing metaheuristic optimizations such as Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By employing a multi-angle analysis based on the cluster validation indices,the confusion matrix,the efficiencies and purities rates,the average cost variation,the computational time and the Sammon mapping visualization,the results highlight the effectiveness of the improved Gustafson-Kessel algorithm optimized withALO(ALOGK)to validate the proposed *** if the paper gives a complete clustering analysis,its novel contribution concerns only the Steps(1)and(3)considered *** first contribution lies in the method used for Step(1)to select the most informative features and *** used the t-Statistic technique to rank ***,a feature mapping is applied using Self-Organizing Map(SOM)to identify the level of correlation between ***,Particle Swarm Optimization(PSO),a metaheuristic optimization technique,is used to reduce the data set *** second contribution of thiswork concern the third step,where each one of the clustering algorithms as K-means(KM),Global K-means(GlobalKM),Partitioning AroundMedoids(PAM),Fuzzy C-means(FCM),Gustafson-Kessel(GK)and Gath-Geva(GG)is optimized and tuned with ALO.

关键词： Ant lion optimization binary clustering clustering algorithms Higgs boson feature extraction dimensionality reduction elbow criterion genetic algorithm particle swarm optimization

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Hybrid Global Optimization Algorithm for Feature Selection

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Computers, Materials & Continua 2023年第1期74卷 2021-2037页

作者： ahmad taher azar Zafar Iqbal Khan Syed Umar Amin Khaled M.Fouad Faculty of Computers and Artificial Intelligence Benha UniversityBenha13511Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Technology and Computer Science Nile UniversitySheikh ZaidEgypt

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization *** experimental study is performed in two stages to assess the proposed *** I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO *** based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)*** phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT *** phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical *** experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable *** from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.

关键词： Particle swarm optimization(PSO) time-variant acceleration coefficients(TVAC) genetic algorithms differential evolution feature selection medical data

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Rough Sets Hybridization with Mayfly Optimization for Dimensionality Reduction

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Computers, Materials & Continua 2022年第10期73卷 1087-1108页

作者： ahmad taher azar Mustafa Samy Elgendy Mustafa Abdul Salam Khaled M.Fouad Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Computer Studies Arab Open UniversityCairoEgypt Faculty of Information Technology and Computer Science Nile UniversityShikh ZaidEgypt

Big data is a vast amount of structured and unstructured data that must be dealt with on a regular *** reduction is the process of converting a huge set of data into data with tiny dimensions so that equal information may be expressed *** tactics are frequently utilized to improve classification or regression challenges while dealing with machine learning *** achieve dimensionality reduction for huge data sets,this paper offers a hybrid particle swarm optimization-rough set PSO-RS and Mayfly algorithm-rough set MA-RS.A novel hybrid strategy based on the Mayfly algorithm(MA)and the rough set(RS)is proposed in *** performance of the novel hybrid algorithm MA-RS is evaluated by solving six different data sets from the *** simulation results and comparison with common reduction methods demonstrate the proposed MARS algorithm’s capacity to handle a wide range of data ***,the rough set approach,as well as the hybrid optimization techniques PSO-RS and MARS,were applied to deal with the massive data ***-hybrid RS’s method beats other classic dimensionality reduction techniques,according to the experimental results and statistical testing studies.

关键词： Dimensionality reduction metaheuristics optimization algorithm mayfly particle swarm optimizer feature selection

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Rooted Tree Optimization for Wind Turbine Optimum Control Based on Energy Storage System

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Computers, Materials & Continua 2023年第2期74卷 3977-3996页

作者： Billel Meghni Afaf Benamor Oussama Hachana ahmad taher azar Amira Boulmaiz Salah Saad El-Sayed M.El-kenawy Nashwa ahmad Kamal Suliman Mohamed Fati Naglaa K.Bahgaat Department of Electrical Engineering Badji Mokhtar UniversityLSEM LaboratoryAnnaba23000Algeria Department of Electrical Engineering Biskra UniversityLGEB LaboratoryBiskra07000Algeria Department of Drilling and Rig Mechanics Ksadi Merbah UniversityOuargla30000Algeria College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt Department of Electronics University of Badji MokhtarLERICA LaboratoryAnnaba23000Algeria Delta Higher Institute for Engineering&Technology(DHIET) Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Faculty of Engineering Cairo UniversityGiza12613Egypt Department of Communications and Electronics Engineering Faculty of EngineeringCanadian International College(CIC)ElShiekh ZayedEgypt

The integration of wind turbines(WTs)in variable speed drive systems belongs to the main factors causing lowstability in electrical ***,in order to avoid this issue,WTs hybridization with a storage system is a *** paper investigates WT system operating at variable *** system contains of a permanent magnet synchronous generator(PMSG)supported by a battery storage system(BSS).To enhance the quality of active and reactive power injected into the network,direct power control(DPC)scheme utilizing space-vector modulation(SVM)technique based on proportional-integral(PI)control is ***,to improve the rendition of this method(DPC-SVM-PI),the rooted tree optimization technique(RTO)algorithm-based controller parameter identification is used to achieve PI optimal *** compare the performance ofRTO-based controllers,they were implemented and tested along with some other popular controllers under different working *** obtained results have shown the supremacy of the suggested PIRTO algorithm compared to competing controllers regarding total harmonic distortion(THD),overshoot percentage,settling time,rise time,average active power value,overall efficiency,and active power steadystate error.

关键词： Rooted tree optimization(RTO)method direct power control(DPC) wind turbine(WT) proportional integral(PI) PMSG

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Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

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Computers, Materials & Continua 2023年第4期75卷 1883-1900页

作者： Ghada Atteia El-Sayed M.El-kenawy Nagwan Abdel Samee Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid ahmad taher azar Nima Khodadadi Reham A.Ghanem Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Civil Engineering University of Science and TechnologyMiami33101USA Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Oral Biology Department Faculty of Oral and Dental MedicineDelta University for Science and TechnologyGamasaEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical *** recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death *** this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

关键词： Medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection meta-heuristics

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Polynomial robust observer implementation based passive synchronization of nonlinear fractional-order systems with structural disturbances

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Frontiers of Information Technology & Electronic Engineering 2020年第9期21卷 1369-1386页

作者： Alain Soup Tewa KAMMOGNE Michaux NoubéKOUNTCHOU Romanic KENGNE ahmad taher azar Hilaire Bertrand FOTSIN Soup Teoua Michael OUAGNI LAMACETS Faculty of SciencesUniversity of DschangP.O.Box 96Cameroon Nuclear Technology Section Institute of Geological and Mining ResearchP.O.Box 4110YaoundéCameroon Robotics and Internet-of-Things Lab(RIOTU) Prince Sultan UniversityRiyadh 11586Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha 13511Egypt Laboratoire de Mécanique et de Modélisation des Systèmes Physique Faculty of SciencesUniversity of DschangP.O.Box 96Cameroon

A robust polynomial observer is designed based on passive synchronization of a given class of fractional-order Colpitts(FOC)systems with mismatched uncertainties and *** primary objective of the proposed observer is to minimize the effects of unknown bounded disturbances on the estimation of errors.A more practicable output-feedback passive controller is proposed using an adaptive polynomial state *** distributed approach of a continuous frequency of the FOC is considered to analyze the stability of the *** we derive some stringent conditions for the robust passive synchronization using Finsler’s lemma based on the fractional Lyapunov stability *** is shown that the proposed method not only guarantees the asymptotic stability of the controller but also allows the derived adaptation law to remove the uncertainties within the nonlinear plant’s *** entire system using passivity is implemented with details in PSpice to demonstrate the feasibility of the proposed control *** results of this research are illustrated using computer simulations for the control problem of the fractional-order chaotic Colpitts *** proposed approach depicts an efficient and systematic control procedure for a large class of nonlinear systems with the fractional derivative.

关键词： Robust passive observer Adaptive synchronization Lyapunov theory Fractional-order Polynomial observer Uncertain parameters H∞-performance

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Facile cathodic electrosynthesis and characterization of iron oxide nano-particles

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Progress in Natural Science:Materials International 2013年第1期23卷 51-54页

作者： taher Yousefi Reza Davarkhah ahmad Nozad Golikand Mohammad Hossein Mashhadizadeh ahmad Abhari NFCRS Nuclear Science and Technology Research InstituteP.O.Box 11365/8486TehranIran Materials Research School NSTRIP.O.Box:14395-836 TehranIran 3. Department of ChemistryTarbiat Moallem UniversityP.O.Box:31979-37551 TehranIran 4. NFCRSNuclear Science and Technology Research InstituteP.O.Box 11365/8486TehranIranx

Fe2O3 nano-particles have been synthesized by simple cathodic electrodeposition from the low-temperature nitrate *** morphology and crystal structure of the obtained oxide powder were analyzed by means of scanning and transmission microscopy(SEM and TEM),X-ray diffraction(XRD) and Fourier transform infrared(FTIR) *** behavior and phase transformation during the heat treatment of as-deposited sample were investigated by differential scanning calorimetry(DSC) and thermogramimetric analysis(TGA).The results showed that the deposited Fe2O3 was composed of the nanoparticles with grain size of approximately 10-60 nm.A serious problem during cathodic electrodeposition of iron oxide was splashing of deposit into electrolyte due to its low *** problem was tackled by reducing the bath temperature and dielectric constant of solvent.

关键词： Iron Oxide Electrochemical Deposition Nano Oxides Nanostructures Characterization

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Application of honey-bee mating optimization on state estimation of a power distribution system including distributed generators

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2008年第12期9卷 1753-1764页

作者： taher NIKNAM Electronic and Electrical Department Shiraz University of TechnologyShirazIran

We present a new approach based on honey-bee mating optimization to estimate the state variables in distribution networks including distributed generators. The proposed method considers practical models of electrical equipments such as static var compensators, voltage regulators, and under-load tap changer transformers, which have usually nonlinear and discrete characteristics. The feasibility of the proposed approach is demonstrated by comparison with the methods based on neural networks, ant colony optimization, and genetic algorithms for two test systems, a network with 34-bus radial test feeders and a realistic 80-bus 20 kV network.

关键词： Distributed generators (DGs) State estimation Honey-bee mating optimization (HBMO)

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AN IMPRINTED POLYMER FOR REMOVAL OF Cd^(2+) FROM WATER SAMPLES:OPTIMIZATION OF ADSORPTION AND RECOVERY STEPS BY EXPERIMENTAL DESIGN

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Chinese Journal of Polymer Science 2011年第6期29卷 658-669页

作者： taher Alizadeh Department of Applied Chemistry Faculty of ScienceUniversity of Mohaghegh ArdabiliArdabilIran

A new ion-imprinted polymer （liP） was synthesized by copolymerization of 4-vinylpyridine （monomer）, ethyleneglycoldimethacrylate （cross-linker） and 2,2-azobis-isobutyronitrile （initiator） in the presence of Cd2＋ and quinaldic acid （complexing agent）. It was found that the adsorption capacity of IIP and blank polymer were 45.0 and 6.2 mg g-l, respectively. The relative selectivity coefficients of the imprinted polymer for different binary mixture were also calculated. Compared to non-imprinted polymer （NIP）, the IIP had higher selectivity for Cd（II）. The IIP was used as a sorbent for cadmium extraction from water samples by using a simple batch extraction procedure. The effect of different parameters on Cd2＋ extraction and its recovery from the IIP were evaluated and optimized by using experimental design methodology. The optimized adsorption/desorption procedure was applied for cadmium removal from the real water samples. The obtained recoveries proved that this IIP could be used for removal of trace cadmium ions from water samples.

关键词： Cd2+ removal Ion imprinted polymer Solid phase extraction Experimental design

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