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  • Open Access

    ARTICLE

    Modified PSO Algorithm on Recurrent Fuzzy Neural Network for System Identification

    Chung Wen Hung, Wei Lung Mao, Han Yi Huang

    Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 329-341, 2019, DOI:10.31209/2019.100000093

    Abstract Nonlinear system modeling and identification is the one of the most important areas in engineering problem. The paper presents the recurrent fuzzy neural network (RFNN) trained by modified particle swarm optimization (MPSO) methods for identifying the dynamic systems and chaotic observation prediction. The proposed MPSO algorithms mainly modify the calculation formulas of inertia weights. Two MPSOs, namely linear decreasing particle swarm optimization (LDPSO) and adaptive particle swarm optimization (APSO) are developed to enhance the convergence behavior in learning process. The RFNN uses MPSO based method to tune the parameters of the membership functions, and it uses gradient descent (GD) based… More >

  • Open Access

    ARTICLE

    A Novel Quantum-Behaved Particle Swarm Optimization Algorithm

    Tao Wu1, Lei Xie1, Xi Chen2, Amir Homayoon Ashrafzadeh3, Shu Zhang4, *

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 873-890, 2020, DOI:10.32604/cmc.2020.07478

    Abstract The efficient management of ambulance routing for emergency requests is vital to save lives when a disaster occurs. Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is a kind of metaheuristic algorithms applied to deal with the problem of scheduling. This paper analyzed the motion pattern of particles in a square potential well, given the position equation of the particles by solving the Schrödinger equation and proposed the Binary Correlation QPSO Algorithm Based on Square Potential Well (BCQSPSO). In this novel algorithm, the intrinsic cognitive link between particles’ experience information and group sharing information was created by using normal Copula function. After… More >

  • Open Access

    ARTICLE

    Improvement of Stochastic Competitive Learning for Social Network

    Wenzheng Li1, Yijun Gu1, *

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 755-768, 2020, DOI:10.32604/cmc.2020.07984

    Abstract As an unsupervised learning method, stochastic competitive learning is commonly used for community detection in social network analysis. Compared with the traditional community detection algorithms, it has the advantage of realizing the timeseries community detection by simulating the community formation process. In order to improve the accuracy and solve the problem that several parameters in stochastic competitive learning need to be pre-set, the author improves the algorithms and realizes improved stochastic competitive learning by particle position initialization, parameter optimization and particle domination ability self-adaptive. The experiment result shows that each improved method improves the accuracy of the algorithm, and the… More >

  • Open Access

    ARTICLE

    Implementation of PSOANN Optimized PI Control Algorithm for Shunt Active Filter

    M. Sujith1, *, S. Padma2

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.3, pp. 863-888, 2020, DOI:10.32604/cmes.2020.08908

    Abstract This paper proposes the optimum controller for shunt active filter (SAF) to mitigate the harmonics and maintain the power quality in the distribution system. It consists of shunt active filter, Voltage Source Inverter (VSI), series inductor and DC bus and nonlinear load. The proposed hybrid approach is a combination of Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) termed as PSOANN. The PI controller gain parameters of kp and ki are optimized with the help of PSOANN. The PSOANN improves the accuracy of tuning the gain parameters under steady and dynamic load conditions; thereby it reduces the values of… More >

  • Open Access

    ARTICLE

    Optimization Design of RC Ribbed Floor System Using Eagle Strategy with Particle Swarm Optimization

    Jiejiang Zhu1, *, Bolun Zhou1

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 365-383, 2020, DOI:10.32604/cmc.2020.06655

    Abstract The eagle strategy algorithm is combined with particle swarm optimization in this paper. The new algorithm, denoted as the ES-PSO, is implemented by interfacing Etabs structural analysis codes. ES-PSO is used to optimize the RC ribbed floor system, including floor and underground garage roof. By considering the effects of reinforcement, the principle of virtual work is applied to calculate the deflections of components. Construction cost is taken as the objective function and the constraint conditions are required to satisfy. Accordingly, the optimal layout, the optimal sections of the beams and slabs and the corresponding reinforcements are obtained for different column… More >

  • Open Access

    ARTICLE

    Aircraft Structural Integrity Assessment through Computational Intelligence Techniques

    RamanaM. Pidaparti1

    Structural Durability & Health Monitoring, Vol.2, No.3, pp. 131-148, 2006, DOI:10.3970/sdhm.2006.002.131

    Abstract This paper provides an overview of the computational intelligence methods developed for the structural integrity assessment of aging aircraft structures. Computational intelligence techniques reviewed include artificial neural networks, inverse neural network mapping, wavelet based image processing methods, genetic algorithms, spectral element methods, and particle swarm optimization. Multi-site damage, corrosion, and corrosion-fatigue damage in aging aircraft is specifically discussed. Results obtained from selected computational intelligence methods are presented and compared to the existing alternate solutions and experimental data. The results presented illustrate the applicability of computational intelligence methods for assessing the structural integrity of aging aircraft structures and materials. More >

  • Open Access

    ARTICLE

    CPAC: Energy-Efficient Algorithm for IoT Sensor Networks Based on Enhanced Hybrid Intelligent Swarm

    Qi Wang1,*, Wei Liu1, Hualong Yu1, Shang Zheng1, Shang Gao1, Fabrizio Granelli2

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.1, pp. 83-103, 2019, DOI:10.32604/cmes.2019.06897

    Abstract The wireless sensor network (WSN) is widely employed in the application scenarios of the Internet of Things (IoT) in recent years. Extending the lifetime of the entire system had become a significant challenge due to the energy-constrained fundamental limits of sensor nodes on the perceptual layer of IoT. The clustering routing structures are currently the most popular solution, which can effectively reduce the energy consumption of the entire network and improve its reliability. This paper introduces an enhanced hybrid intelligential algorithm based on particle swarm optimization (PSO) and ant colony optimization (ACO) method. The enhanced PSO is deployed to select… More >

  • Open Access

    ARTICLE

    Analysis of OSA Syndrome from PPG Signal Using CART-PSO Classifier with Time Domain and Frequency Domain Features

    N. Kins Burk Sunil1, *, R. Ganesan2, B. Sankaragomathi3

    CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.2, pp. 351-375, 2019, DOI:10.31614/cmes.2018.04484

    Abstract Obstructive Sleep Apnea (OSA) is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation. The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea (SA) activity. In the proposed method, the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted. These features are applied to the Classification and Regression Tree (CART)-Particle Swarm Optimization (PSO) classifier which classifies the signal into normal breathing signal… More >

  • Open Access

    ARTICLE

    A PSO based Energy Efficient Coverage Control Algorithm for Wireless Sensor Networks

    Jin Wang1,2, Chunwei Ju2, Yu Gao2, Arun Kumar Sangaiah3, Gwang-jun Kim4,*

    CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 433-446, 2018, DOI: 10.3970/cmc.2018.04132

    Abstract Wireless Sensor Networks (WSNs) are large-scale and high-density networks that typically have coverage area overlap. In addition, a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area, which leads to coverage holes in WSNs. Thus, coverage control plays an important role in WSNs. To alleviate unnecessary energy wastage and improve network performance, we consider both energy efficiency and coverage rate for WSNs. In this paper, we present a novel coverage control algorithm based on Particle Swarm Optimization (PSO). Firstly, the sensor nodes are randomly deployed in a target area and remain static after deployment. Then, the… More >

  • Open Access

    ARTICLE

    Improved Particle Swarm Optimization for Selection of Shield Tunneling Parameter Values

    Gongyu Hou1, Zhedong Xu1,*, Xin Liu1, Cong Jin1

    CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.2, pp. 317-337, 2019, DOI:10.31614/cmes.2019.04693

    Abstract This article proposes an exponential adjustment inertia weight immune particle swarm optimization (EAIW-IPSO) to enhance the accuracy and reliability regarding the selection of shield tunneling parameter values. According to the iteration changes and the range of inertia weight in particle swarm optimization algorithm (PSO), the inertia weight is adjusted by the form of exponential function. Meanwhile, the self-regulation mechanism of the immune system is combined with the PSO. 12 benchmark functions and the realistic cases of shield tunneling parameter value selection are utilized to demonstrate the feasibility and accuracy of the proposed EAIW-IPSO algorithm. Comparison with other improved PSO indicates… More >

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