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  • Open Access


    New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

    Shimaa M. Amer1, Ashraf A. M. Khalaf2, Amr H. Hussein3,4, Salman A. Alqahtani5, Mostafa H. Dahshan6, Hossam M. Kassem3,4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2749-2767, 2024, DOI:10.32604/cmes.2023.029138

    Abstract Side lobe level reduction (SLL) of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service (QOS) in recent and future wireless communication systems starting from 5G up to 7G. Furthermore, it improves the array gain and directivity, increasing the detection range and angular resolution of radar systems. This study proposes two highly efficient SLL reduction techniques. These techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm (GA) to develop the Conv/GA and DConv/GA, respectively. The convolution process determines the element’s… More >

  • Open Access


    Hybrid Optimization Algorithm for Resource Allocation in LTE-Based D2D Communication

    Amel Austine*, R. Suji Pramila

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2263-2276, 2023, DOI:10.32604/csse.2023.032323

    Abstract In a cellular network, direct Device-to-Device (D2D) communication enhances Quality of Service (QoS) in terms of coverage, throughput and amount of power consumed. Since the D2D pairs involve cellular resources for communication, the chances of interference are high. D2D communications demand minimum interference along with maximum throughput and sum rate which can be achieved by employing optimal resources and efficient power allocation procedures. In this research, a hybrid optimization model called Genetic Algorithm-Adaptive Bat Optimization (GA-ABO) algorithm is proposed for efficient resource allocation in a cellular network with D2D communication. Simulation analysis demonstrates that the More >

  • Open Access


    An Adaptive Genetic Algorithm-Based Load Balancing-Aware Task Scheduling Technique for Cloud Computing

    Mohit Agarwal1,*, Shikha Gupta2

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6103-6119, 2022, DOI:10.32604/cmc.2022.030778

    Abstract Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers. Task scheduling algorithms are responsible for the allocation of the tasks among the computing resources for their execution, and an inefficient task scheduling algorithm results in under-or over-utilization of the resources, which in turn leads to degradation of the services. Therefore, in the proposed work, load balancing is considered as an important criterion for task scheduling in a cloud computing environment as it can help in reducing the overhead in the critical decision-oriented process.… 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… More >

  • Open Access


    An Improved Genetic Algorithm for Automated Convolutional Neural Network Design

    Rahul Dubey*, Jitendra Agrawal

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 747-763, 2022, DOI:10.32604/iasc.2022.020975

    Abstract Extracting the features from an image is a cumbersome task. Initially, this task was performed by domain experts through a process known as handcrafted feature design. A deep embedding technique known as convolutional neural networks (CNNs) later solved this problem by introducing the feature learning concept, through which the CNN is directly provided with images. This CNN then learns the features of the image, which are subsequently given as input to the further layers for an intended task like classification. CNNs have demonstrated astonishing performance in several practicable applications in the last few years. Nevertheless,… More >

  • Open Access


    Improving the Morphological Parameters of Aluminum Foam for Maximum Sound Absorption Coefficient using Genetic Algorithm

    Mohammad Javad Jafari1, Mohsen Niknam Sharak2, Ali Khavanin3, Touraj Ebadzadeh4, Mahmood Fazlali5, Rohollah Fallah Madvari6,*

    Sound & Vibration, Vol.55, No.2, pp. 117-130, 2021, DOI:10.32604/sv.2021.09729

    Abstract Fabricating of metal foams with desired morphological parameters including pore size, porosity and pore opening is possible now using sintering technology. Thus, if it is possible to determine the morphology of metal foam to absorb sound at a given frequency, and then fabricate it through sintering, it is expected to have optimized metal foams for the best sound absorption. Theoretical sound absorption models such as Lu model describe the relationship between morphological parameters and the sound absorption coefficient. In this study, the Lu model was used to optimize the morphological parameters of Aluminum metal foam… More >

  • Open Access


    Genetic Algorithm and Tabu Search Memory with Course Sandwiching (GATS_CS) for University Examination Timetabling

    Abayomi-Alli A.1, Misra S.2,3, Fernández-Sanz L.4, Abayomi-Alli O.2,*, Edun A. R.1

    Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 385-396, 2020, DOI:10.32604/iasc.2020.013915

    Abstract University timetable scheduling is a complicated constraint problem because educational institutions use timetables to maximize and optimize scarce resources, such as time and space. In this paper, an examination timetable system using Genetic Algorithm and Tabu Search memory with course sandwiching (GAT_CS), was developed for a large public University. The concept of Genetic Algorithm with Selection and Evaluation was implemented while the memory properties of Tabu Search and course sandwiching replaced Crossover and Mutation. The result showed that GAT_CS had hall allocation accuracies of 96.07% and 99.02%, unallocated score of 3.93% and 0.98% for first More >

  • Open Access


    Determination of Temperature-Dependent Elasto-Plastic Properties of Thin-Film by MD Nanoindentation Simulations and an Inverse GA/FEM Computational Scheme

    D. S. Liu1, C. Y. Tsai1, S. R. Lyu2

    CMC-Computers, Materials & Continua, Vol.11, No.2, pp. 147-164, 2009, DOI:10.3970/cmc.2009.011.147

    Abstract This study presents a novel numerical method for extracting the tempe -rature-dependent mechanical properties of the gold and aluminum thin-films. In the proposed approach, molecular dynamics (MD) simulations are performed to establish the load-displacement response of the thin substrate nanoindented at temperatures ranging from 300-900 K. A simple but effective procedure involving genetic algorithm (GA) and finite element method (FEM) is implemented to extract the material constants of the gold and aluminum substrates. The material constants are then used to construct the corresponding stress-strain curve, from which the elastic modulus, yield stress and the tangent More >

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