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 >

Yasir Mehmood, Waseem Shahzad

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 91-103, 2019, DOI:10.31209/2018.100000017

Abstract Particle swarm optimization (PSO) algorithm is a global optimization technique that is used to find the optimal solution in multimodal problems. However, one of the limitation of PSO is its slow convergence rate along with a local trapping dilemma in complex multimodal problems. To address this issue, this paper provides an alternative technique known as ACPSO algorithm, which enables to adopt a new simplified velocity update rule to enhance the performance of PSO. As a result, the efficiency of convergence speed and solution accuracy can be maximized. The experimental results show that the ACPSO outperforms most of the compared PSO… More >

Amit Chhabra^{1, *}, Gurvinder Singh², Karanjeet Singh Kahlon²

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 813-834, 2020, DOI:10.32604/cmc.2020.010934

Abstract Cloud computing infrastructure has been evolving as a cost-effective platform for providing computational resources in the form of high-performance computing as a service (HPCaaS) to users for executing HPC applications. However, the broader use of the Cloud services, the rapid increase in the size, and the capacity of Cloud data centers bring a remarkable rise in energy consumption leading to a significant rise in the system provider expenses and carbon emissions in the environment. Besides this, users have become more demanding in terms of Quality-of-service (QoS) expectations in terms of execution time, budget cost, utilization, and makespan. This situation calls… More >

Tao Wu¹, Lei Xie¹, Xi Chen², Amir Homayoon Ashrafzadeh³, Shu Zhang^{4, *}

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 >

Wenzheng Li¹, Yijun Gu^{1, *}

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 >

Yunping Liu^{1, 2, *}, Xingxing Yan¹, Fei Yan¹, Ze Xu¹, Weiyan Shang³

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 469-487, 2020, DOI:10.32604/cmc.2020.05746

Abstract Due to the coupled motion between the rotor unmanned aerial vehicle (UAV) and the manipulator, the underactuation characteristics of the system itself, and the influence of external uncertainties, the stability of the rotor UAV’s manipulator control system is difficult to control. Based on the dynamic model of the rotor UAV, the stability of the whole UAV manipulator control system is improved by using the piecewise cost function, the compression factor particle swarm optimization (PSO) algorithm and the sliding mode PID to establish the sliding mode PID control stability method based on the PSO. Compared with the sliding mode PID control… More >

M. Sujith^{1, *}, 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 k_p and k_i 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 >

Jiejiang Zhu^{1, *}, 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 >

Jiarui Zhang¹, Gang Wang^2,*, Siyuan Tong¹

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1091-1104, 2019, DOI:10.32604/cmc.2019.05907

Abstract Aiming at the problem of gate allocation of transit flights, a flight first service model is established. Under the constraints of maximizing the utilization rate of gates and minimizing the transit time, the idea of “first flight serving first” is used to allocate the first time, and then the hybrid algorithm of artificial fish swarm and simulated annealing is used to find the optimal solution. That means the fish swarm algorithm with the swallowing behavior is employed to find the optimal solution quickly, and the simulated annealing algorithm is used to obtain a global optimal allocation scheme for the optimal… More >

A. Rama Mohan Rao¹, K. Lakshmi¹, Karthik Ganesan²

Structural Durability & Health Monitoring, Vol.9, No.2, pp. 99-128, 2013, DOI:10.32604/sdhm.2013.009.099

Abstract Development of efficient system identification techniques is highly relevant for large civil infrastructure for effective health monitoring, damage detection and vibration control. This paper presents a system identification scheme in time domain to estimate stiffness and damping parameters of structures using measured acceleration. Instead of solving the system identification problem as an inverse problem, we formulate it as an optimisation problem. Particle swarm optimisation (PSO) and its other variants has been a subject of research for the past few decades for solving complex optimisation problems. In this paper, a dynamic quantum behaved particle swarm optimisation algorithm (DQPSO) is proposed for… More >