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  • Open Access

    ARTICLE

    Particle Swarm Optimization with New Initializing Technique to Solve Global Optimization Problems

    Adnan Ashraf1, Abdulwahab Ali Almazroi2, Waqas Haider Bangyal3,*, Mohammed A. Alqarni4

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 191-206, 2022, DOI:10.32604/iasc.2022.015810 - 03 September 2021

    Abstract Particle Swarm Optimization (PSO) is a well-known extensively utilized algorithm for a distinct type of optimization problem. In meta-heuristic algorithms, population initialization plays a vital role in solving the classical problems of optimization. The population’s initialization in meta-heuristic algorithms urges the convergence rate and diversity, besides this, it is remarkably beneficial for finding the efficient and effective optimal solution. In this study, we proposed an enhanced variation of the PSO algorithm by using a quasi-random sequence (QRS) for population initialization to improve the convergence rate and diversity. Furthermore, this study represents a new approach for… More >

  • Open Access

    ARTICLE

    Multi-Objective High-Fidelity Optimization Using NSGA-III and MO-RPSOLC

    N. Ganesh1, Uvaraja Ragavendran2, Kanak Kalita3,*, Paras Jain4, Xiao-Zhi Gao5

    CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 443-464, 2021, DOI:10.32604/cmes.2021.014960 - 08 October 2021

    Abstract Optimizing the performance of composite structures is a real-world application with significant benefits. In this paper, a high-fidelity finite element method (FEM) is combined with the iterative improvement capability of metaheuristic optimization algorithms to obtain optimized composite plates. The FEM module comprises of ninenode isoparametric plate bending element in conjunction with the first-order shear deformation theory (FSDT). A recently proposed memetic version of particle swarm optimization called RPSOLC is modified in the current research to carry out multi-objective Pareto optimization. The performance of the MO-RPSOLC is found to be comparable with the NSGA-III. This work More >

  • Open Access

    ARTICLE

    Hybrid Swarm Intelligence Based QoS Aware Clustering with Routing Protocol for WSN

    M. S. Maharajan1, T. Abirami2, Irina V. Pustokhina3, Denis A. Pustokhin4, K. Shankar5,*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2995-3013, 2021, DOI:10.32604/cmc.2021.016139 - 06 May 2021

    Abstract Wireless Sensor Networks (WSN) started gaining attention due to its wide application in the fields of data collection and information processing. The recent advancements in multimedia sensors demand the Quality of Service (QoS) be maintained up to certain standards. The restrictions and requirements in QoS management completely depend upon the nature of target application. Some of the major QoS parameters in WSN are energy efficiency, network lifetime, delay and throughput. In this scenario, clustering and routing are considered as the most effective techniques to meet the demands of QoS. Since they are treated as NP… More >

  • Open Access

    ARTICLE

    IWD-Miner: A Novel Metaheuristic Algorithm for Medical Data Classification

    Sarab AlMuhaideb*, Reem BinGhannam, Nourah Alhelal, Shatha Alduheshi, Fatimah Alkhamees, Raghad Alsuhaibani

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1329-1346, 2021, DOI:10.32604/cmc.2020.013576 - 26 November 2020

    Abstract Medical data classification (MDC) refers to the application of classification methods on medical datasets. This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis. To gain experts’ trust, the prediction and the reasoning behind it are equally important. Accordingly, we confine our research to learn rule-based models because they are transparent and comprehensible. One approach to MDC involves the use of metaheuristic (MH) algorithms. Here we report on the development and testing of a novel MH algorithm: IWD-Miner. This algorithm can… More >

  • Open Access

    ARTICLE

    Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

    Gopi Krishna Durbhaka1, Barani Selvaraj1, Mamta Mittal2, Tanzila Saba3,*, Amjad Rehman3, Lalit Mohan Goyal4

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2041-2059, 2021, DOI:10.32604/cmc.2020.013131 - 26 November 2020

    Abstract Nowadays, renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs. Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task. Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches, practices and technology during the last decade. Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect. This paper proposes a new hybrid model wherein multiple swarm intelligence models have More >

  • Open Access

    ARTICLE

    A Novel Fault Tolerance Energy-Aware Clustering Method via Social Spider Optimization (SSO) and Fuzzy Logic and Mobile Sink in Wireless Sensor Networks (WSNs)

    Shayesteh Tabatabaei1,∗

    Computer Systems Science and Engineering, Vol.35, No.6, pp. 477-494, 2020, DOI:10.32604/csse.2020.35.477

    Abstract In recent years, the application of WSNs has been remarkably increased and notable developments and advances have been achieved in this regard. In particular, thanks to smart, cheaper and smaller nodes, different types of information can be detected and gathered in different environments and under different conditions. As the popularity of WSNs has increased, the problems and issues related to networks are examined and investigated. As a case in point, routing issue is one of the main challenges in this regard which has a direct impact on the performance of sensor networks. In WSN routing,… More >

  • Open Access

    ARTICLE

    Improvement of the Firework Algorithm for Classification Problems

    Yu Xue, Sow Alpha Amadou*, Yan Zhao

    Journal of Cyber Security, Vol.2, No.4, pp. 191-196, 2020, DOI:10.32604/jcs.2020.014045 - 07 December 2020

    Abstract Attracted numerous analysts’ consideration, classification is one of the primary issues in Machine learning. Numerous evolutionary algorithms (EAs) were utilized to improve their global search ability. In the previous years, many scientists have attempted to tackle this issue, yet regardless of the endeavors, there are still a few inadequacies. Based on solving the classification problem, this paper introduces a new optimization classification model, which can be applied to the majority of evolutionary computing (EC) techniques. Firework algorithm (FWA) is one of the EC methods, Although the Firework algorithm (FWA) is a proficient algorithm for solving More >

  • Open Access

    ARTICLE

    Evolved Algorithm and Vibration Stability for Nonlinear Disturbed Security Systems

    Tcw Chen1, Wray Marriott2, Ann Nicholson3, Tim Chen4, Mars Kmieckowiak5, Jcy Chen6,*

    Sound & Vibration, Vol.53, No.2, pp. 29-37, 2019, DOI:10.32604/sv.2019.04224

    Abstract In this paper, a method sustaining system stability after decomposition is proposed. Based on the stability criterion derived from the energy function, a set of intelligent controllers is synthesized which is used to maintain the stability of the system. The sustainable stability problem can be reformulated as a Linear Matrix Inequalities (LMI) problem. The key to guaranteeing the stability of the system as a whole is to find a common symmetrically positive definite matrix for all subsystems. Furthermore, the Evolved Bat Algorithm (EBA) is employed to replace the pole assignment method and the conventional mathematical More >

  • Open Access

    ARTICLE

    Suppression of Ambipolar Conduction in Schottky Barrier Carbon Nanotube Field Effect Transistors: Modeling, Optimization Using Particle Swarm Intelligence, and Fabrication

    P. Reena Monica1,*, V. T. Sreedevi2

    CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 577-591, 2019, DOI:10.32604/cmes.2019.04718

    Abstract A mathematical model and experimental analysis of the impact of oxide thickness on the ambipolar conduction in Schottky Barrier Carbon Nanotubes (CNTs) Field Effect Transistor (SB CNTFETs) is presented. Suppression of ambipolar conduction in SB CNTFETs is imperative in order to establish them as the future of IC technology. The ambipolar nature of SB CNTFETs leads to a great amount of leakage current. Employing a gate oxide dielectric of thickness, tox~50 nm suppresses the ambipolar behavior. In an SB CNTFET, it is the electric field at the source/drain contacts that control the conductance and the band… More >

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