Papers by Keyword: Particle Swarm Optimization (PSO)

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Authors: Chang Liang Liu, Meng Ni
Abstract: In order to solve the problems of strong coupling and nonlinear parameters of superheater model, in this paper, we aimed at a 600MW subcritical drum boiler high temperature superheater as the object of study .On the basis of mechanism analysis and its dynamic characteristics I built a dynamic model of high superheater contains 9 typical working conditions .I optimized the model parameters by using field data. A continuous nonlinear model is obtained by curve fitting. The simulation results show that superheater model built by this method can better simulate the field characteristic. This model provides a reference design of automatic control system of main steam temperature .This paper aims to provide the basis for the application of advanced control algorithms in the field. Furthermore, this model can provide higher accuracy for on-site training to improve training effect.
Authors: Zhi Dong Wu, Sui Hua Zhou, Shi Min Feng, Zu Jing Xiao
Abstract: To overcome the shortage that the particle swarm optimization is prone to trap into local extremum searching for the lost in population diversity, a strategy in which the velocity is not dependent on the size of distance between the individual and the optimal particle but only dependent on its direction is proposed. The average similarity of particles in the population is seem as the measure of population diversity and it is used to balance the global and local searching of the algorithm. Based on establishing the relationship between inertia weight and the measure of population diversity which has been inserted into the algorithm, A resilient particle swarm optimization algorithm with dynamically changing inertia weight (ARPSO) was proposed. ARPSO was applied in simulation experiment. The results show that the algorithm has the ability to avoid being trapped in local extremum and advance the probability of finding global optimum.
Authors: Zhan Qi Fan, Lin Liu, Xun Sun
Abstract: A robust flight control method based on the particle swarm optimization (PSO) algorithm is approved in this paper. Because of non-modeling dynamic character and parameter uncertainty are taken into consideration during the controller design process, the flight controller has strong robustness, excellent control performance and one robust controller could realize the large envelope flight control. In order to design the robust flight controller automatically and rapidly, the particle swarm optimization algorithm is used to select the weighting function. The simulation result shows that the weighting function could be designed automatically and rapidly, the flight controller has good performance and robustness.
Authors: Dong Xiao Niu, Qian Zhang
Abstract: This paper examines optimization of revenue of power plants facing stochastic demand with varied prices. A network optimization model is proposed for power plant revenue management under an uncertain environment. The network optimization has a stochastic programming formulation designed to capture the randomness of the unknown demand. A novel approach of robust optimization and PSO are applied to solve the problem on a scenario-basis. Decision-makers risk aversion is considered in the objective function. Mean absolute value is used to measure risk of deviation of revenue from its expected value.
Authors: Zhi Jie Li, Xiao Dong Duan, Cun Rui Wang, Shu Zhe Bao
Abstract: This paper presents an automatic approach for facial expression deformation. In order to guarantee the expression deformation is dealt with high quality and conveniently, the approach uses two key technologies: the PSO detection and the affine transformation. The simulation results show that this method can produce natural smile expression, without marking the spots manually, especially works well for some shy smile expressions.
Authors: Chan Hyok Jong, Guang Wei Meng, Feng Li, Li Ming Zhou, Yan Hao, Yong Su Kong
Abstract: A structural reliability analysis approach for uncertain structures based on a PSO-DE hybrid algorithm was proposed. In order to analyze the structural non-probabilistic reliability for structures with uncertain parameters, an optimization problem by using the convex model and the penalty function method was formulated. For better convergence speed and precision, the particle swarm optimization (PSO) algorithm and the differential evolution (DE) algorithm were combined to solve the structural reliability optimization problem, this PSO-DE hybrid algorithm was based on the evolution of the cognitive experience. The numerical examples were presented to demonstrate the effectiveness and accuracy of the proposed structural reliability analysis method.
Authors: Ming Li Song
Abstract: The complexity of optimization problems encountered in various modeling algorithms makes a selection of a proper optimization vehicle crucial. Developments in particle swarm algorithm since its origin along with its benefits and drawbacks are mainly discussed as particle swarm optimization provides a simple realization mechanism and high convergence speed. We discuss several developments for single-objective case problem and multi-objective case problem.
Authors: Zhen Zhou An, Hui Zhou, Yang Yang, Xin Ling Shi
Abstract: For studying the sensitivity of PSO to control parameter choices, this paper proposes a special model of PSO theoretically. This model divides the position sequence of particle into the odd and even sub-sequences. The theorem demonstrates the position sequence of particle is affected by the parameter choices, the initialized position and velocity. Simulations for benchmark functions illustrate the validity of the odd-even property of particle trajectory.
Authors: Yi Liu, Cai Hong Mu, Wei Dong Kou, Jing Liu
Abstract: This paper presents a variant of the particle swarm optimization (PSO) that we call the adaptive particle swarm optimization with dynamic population (DP-APSO), which adopts a novel dynamic population (DP) strategy whereby the population size of swarm can vary with the evolutionary process. The DP strategy enables the population size to increase when the swarm converges and decrease when the swarm disperses. Experiments were conducted on two well-studied constrained engineering design optimization problems. The results demonstrate better performance of the DP-APSO in solving these engineering design optimization problems when compared with two other evolutionary computation algorithms.
Authors: Aqeel S. Jaber, Abu Zaharin B. Ahmad, Ahmed N. Abdalla
Abstract: One of the most important rules in electric power system operation and control is Load Frequency Controller (LFC). Many problems are subject to LFC such as a generating unit is suddenly disconnected by the protection equipment and suddenly large load is connected or disconnected. The frequency gets deviated from nominal value when the real power balance is harmed due to disturbances.LFC is responsible for load balancing and restoring the natural frequency to its natural position. In this paper, PSO-fuzzy logic technique for Load Frequency Control system was proposed. PSO optimization method is used to tuning the input and output gains for the fuzzy controller. The proposed method has been tested on two symmetrical thermal areas of an interconnected electrical power system. The simulation results are carried out in term of effectiveness of the frequency time response on its damping and compared it to common PID controller. The results show the performances of the proposed controller have quite promising compared to PID controller.
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