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Search Results (27)
  • Open Access


    Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

    Doaa Sami Khafaga1, El-Sayed M. El-kenawy2,3, Faten Khalid Karim1,*, Mostafa Abotaleb4, Abdelhameed Ibrahim5, Abdelaziz A. Abdelhamid6,7, D. L. Elsheweikh8

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4531-4545, 2023, DOI:10.32604/cmc.2023.033042

    Abstract Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning. Each feature in a dataset has 2n possible subsets, making it challenging to select the optimum collection of features using typical methods. As a result, a new metaheuristics-based feature selection method based on the dipper-throated and grey-wolf optimization (DTO-GW) algorithms has been developed in this research. Instability can result when the selection of features is subject to metaheuristics, which can lead to a wide range of results. Thus, we adopted hybrid optimization in our method of optimizing, which allowed us… More >

  • Open Access


    E-MOGWO Algorithm for Computation Offloading in Fog Computing

    Jyoti Yadav*, Suman

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1063-1078, 2023, DOI:10.32604/iasc.2023.032883

    Abstract Despite the advances mobile devices have endured, they still remain resource-restricted computing devices, so there is a need for a technology that supports these devices. An emerging technology that supports such resource-constrained devices is called fog computing. End devices can offload the task to close-by fog nodes to improve the quality of service and experience. Since computation offloading is a multiobjective problem, we need to consider many factors before taking offloading decisions, such as task length, remaining battery power, latency, communication cost, etc. This study uses the multiobjective grey wolf optimization (MOGWO) technique for optimizing offloading decisions. This is the… More >

  • Open Access


    Recognizing Ancient South Indian Language Using Opposition Based Grey Wolf Optimization

    A. Naresh Kumar1,*, G. Geetha2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2619-2637, 2023, DOI:10.32604/iasc.2023.028349

    Abstract Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools. This stipulation make the dispensation period overriding, difficult and tiresome to calculate. This paper present a technique for recognizing ancient south Indian languages by applying Artificial Neural Network (ANN) associated with Opposition based Grey Wolf Optimization Algorithm (OGWA). It identifies the prehistoric language, signs and fonts. It is an apparent from the ANN system that arbitrarily produced weights or neurons linking various layers play a significant role in its performance. For adaptively determining these weights, this paper applies various optimization algorithms such as Opposition… More >

  • Open Access


    Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

    S. Siamala Devi1, K. Venkatachalam2, Yunyoung Nam3,*, Mohamed Abouhawwash4,5

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 1869-1880, 2023, DOI:10.32604/csse.2023.025505

    Abstract The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message… More >

  • Open Access


    Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

    Adel Hamdan Mohammad1,*, Tariq Alwada’n2, Omar Almomani3, Sami Smadi3, Nidhal ElOmari4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 133-150, 2022, DOI:10.32604/cmc.2022.027475

    Abstract 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 bio-inspired 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… More >

  • Open Access


    Efficient Single-Stage Bridgeless AC to DC Converter Using Grey Wolf Optimization

    Prema Kandasamy1,*, K. Prem Kumar2

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 487-499, 2022, DOI:10.32604/csse.2022.021693

    Abstract Bridgeless single-stage converters are used for efficient (alternative current) AC-(direct current) DC conversion. These converters control generators, like electromagnetic meso- and micro-scale generators with low voltage. Power factor correction helps increase the factor of the power supply. The main advantage of the power factor is it shapes the input current for increasing the real power of the AC supply. In this paper, a two-switch bridgeless rectifier topology is designed with a power factor correction capability. For the proposed converter topology to have good power quality parameters, the closed loop scheme, which uses the grey wolf optimization (GWO) algorithm, is implemented.… More >

  • Open Access


    Feature Selection Using Grey Wolf Optimization with Random Differential Grouping

    R. S. Latha1,*, B. Saravana Balaji2, Nebojsa Bacanin3, Ivana Strumberger3, Miodrag Zivkovic3, Milos Kabiljo3

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 317-332, 2022, DOI:10.32604/csse.2022.020487

    Abstract Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity. The user’s access over the internet creates massive data processing over the internet. Big data require an intelligent feature selection model by addressing huge varieties of data. Traditional feature selection techniques are only applicable to simple data mining. Intelligent techniques are needed in big data processing and machine learning for an efficient classification. Major feature selection algorithms read the input features as they are. Then, the features are preprocessed and classified. Here, an algorithm does… More >

  • Open Access


    Swarm-Based Extreme Learning Machine Models for Global Optimization

    Mustafa Abdul Salam1,*, Ahmad Taher Azar2, Rana Hussien2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6339-6363, 2022, DOI:10.32604/cmc.2022.020583

    Abstract 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… More >

  • Open Access


    Prediction of Transformer Oil Breakdown Voltage with Barriers Using Optimization Techniques

    Sherif S. M. Ghoneim1,*, Mosleh M. Alharthi1, Ragab A. El-Sehiemy2, Abdullah M. Shaheen3

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1593-1610, 2022, DOI:10.32604/iasc.2022.020464

    Abstract A new procedure to optimally identifying the prediction equation of oil breakdown voltage with the barrier parameters’ effect is presented. The specified equation is built based on the results of experimental works to link the response with the barrier parameters as the inputs for hemisphere-hemisphere electrode gap configuration under AC voltage. The AC HV is applied using HV Transformer Type (PGK HB-100 kV AC) to the high voltage electrode in the presence of a barrier immersed in Diala B insulating oil. The problem is formulated as a nonlinear optimization problem to minimize the error between experimental and estimated breakdown voltages… More >

  • Open Access


    A Grey Wolf Optimized 15-Level Inverter Design with Confined Switching Components

    S. Caroline1,*, M. Marsaline Beno2

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1753-1769, 2022, DOI:10.32604/iasc.2022.020440

    Abstract Multilevel inverters are a new class of dc-ac converters designed for high-power medium voltage and power applications as they work at high switching frequencies and in renewable applications by avoiding stresses like dv/dt and has low harmonic distortion in their output voltage. In variable speed drives and power generation systems, the use of multilevel inverters is obligatory. To estimate the switching positions in inverter configuration with low harmonic distortion value, a fast sequential optimization algorithm has been established. For harmonic reduction in multilevel inverter design, a hybrid optimization technique combining Firefly and the Genetic algorithm was used. In several real-time… More >

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