Papers by Keyword: Particle Swarm Optimization Algorithm (PSO)

Abstract: Direct internal reforming solid oxide fuel cell (DIR-SOFC) is directly fueled with hydrocarbons and converts the chemical energy of the fuel directly into electrical energy. Particle swarm optimization (PSO) with adaptive inertia weights has global search ability and faster convergence rate. Wavelet network (WN) combines the advantage of multi-resolution approximation (MRA) of the wavelet decomposition and the capability of neural networks in learning from nonlinear process. The DIR-SOFC is considered a complicated nonlinear multi-input and multi-output (MIMO) system which contains coupling parameters. A method combines the WN with PSO algorithm is applied to establish the model of DIR-SOFC, avoiding the consideration of the complex process inside the cell. The simulation results show that the obtained dynamic model can accurately simulate the dynamics of the DIR-SOFC.

Abstract: In order to reduce the noise of acquisition signal in laser cutting, an adaptive wavelet denoising method is proposed in this paper. Based on the analysis of the limitations of traditional threshold method, the particle swarm optimization algorithm is used to select the optimal threshold of wavelet. Compared with the commonly hard and soft threshold method, the experiment results show that the method used in this paper is relatively stable, and can reduce noise excellently. The method can provide more accurate signal for quality analysis in laser cutting .So the method can be used in noise denoising of pulse-induced acoustic sound.

Abstract: For traditional methods for coal mine gas emission prediction accuracy is not high, an adaptive mutation particle swarm optimization neural network approach is introduced. The algorithm increases the mutation operation in iterative process, and adaptive adjusts mutation probability of the size, in order to enhance the ability to jump out of the local optima. The simulation results show that the method can be better predicted coal mine gas, has a certain practicality.

Abstract: This paper analyzes the machining process of CNC machine tools, and builds an optimization model of the machining process parameters based on the mechanical vibration and the operational research. The model mixed genetic algorithm and particle swarm optimization (PSO) is built. It proposes an optimization algorithm that has higher convergence precision and execute ability to solve engineering problem with nonlinear and multi-extremum. According to case study, it proves the correctness of the model and the efficiency and high-performance nature of the designed optimization algorithm. It also appears the efficiency to solve the common engineering problems by the intelligent optimization algorithms.

Abstract: We analyze the overall operation process and storage model of U-shaped curve rail type AS/RS, put forward an optimization designing method based on minimal operating time, set up a mathematical model, and make verifications using genetic algorithm which show that this optimization method greatly increases the operating efficiency of U-shaped curve rail type AS/RS, and provides a powerful reference for its application.

Abstract: The driving range of electric vehicles is less than traditional vehicles due to the restriction of energy storage. It is raising the efficiency of each power component that is one of increasing electric vehicle driving range methods. A particle swarm optimization is used to optimize transmission gear ratio on established electric vehicle power component models. A simulation that simulates the energy consumption of vehicle after gear ratio optimization is given to compare with the actual energy consumption data of the vehicle before gear ratio optimization. The results show that the energy consumption and driving range of the latter are better than the former therefore this optimization is valid.

Abstract: A PSO-algorithm-based job scheduling method that takes production cost as optimization object is presented in this paper. The cost optimization model of HFSP, in which production cost is considered as an optimal factor, is constructed. PSO is used to take global optimization, make the production task assignment and find which machine the jobs should be assigned at each stage, which is also called the process route of the job. After that the local assignment rules are used to determine the job’s starting time and processing sequence at each stage. The total production cost converted by time-based scheduling results is comprehensively considering the processing cost, waiting costs, and the products storage costs. The numerical results show the effectiveness of the algorithm after comparing between multi-group programs.

Abstract: A novel image segmentation algorithm based on fuzzy C-means (FCM) clustering and improved particle swarm optimization (PSO) is proposed. The algorithm takes global search results of improved PSO as the initialized values of the FCM, effectively avoiding easily trapping into local optimum of the traditional FCM and the premature convergence of PSO. Meanwhile, the algorithm takes the clustering centers as the reference to search scope of improved PSO algorithm for global searching that are obtained through hard C-means (HCM) algorithm for improving the velocity of the algorithm. The experimental results show the proposed algorithm can converge more quickly and segment the image more effectively than the traditional FCM algorithm.

Abstract: For decentralized ordered statistics (OS) constant false alarm ration (CFAR) detection system, the parameter estimation and performance analysis in complicated detection condition is a typical nonlinear optimization problem. Owing to the nonlinear property of distributed OS-CFAR detection system, it is seriously difficult to obtain optimal threshold values using some optimization method at the fusion center. This paper provides a novel solution based on an effective and flexible particle swarm optimization (PSO) algorithm. As a novel evolutionary computation technique, PSO has attracted much attention and wide applications, owing to its simple concept, easy implementation and quick convergence. Using this approach, all system parameters can be optimized simultaneously. The simulation results show that the proposed approach can achieve effective performances with the above method.

Abstract: To provide quality-of-service (QoS) differentiation and guarantee user fairness, efficient power and spectrum utilization for the downlink multiuser orthogonal frequency-division multiplexing (MU-OFDM) systems, a novel QoS guaranteed cross-layer (QGCL) scheduling scheme is proposed in this paper. The scheme formulates the scheduling into an optimization problem of overall system utility under the system constraints. Moreover, we propose a simple and efficient binary constrained particle swarm optimization (PSO) to solve the scheduling more effectively. Comparing with the classical methods, simulation results show that the proposed QGCL scheduling scheme can significantly maximize the system throughput given that the fulfillment of the QoS requirements as well as the fairness is guaranteed.