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An Improved Particle Swarm Optimization Based on Repulsion Factor  [PDF]
Jie Zhang, Chaozan Fan, Bo Liu, Fugui Shi
Open Journal of Applied Sciences (OJAppS) , 2012, DOI: 10.4236/ojapps.2012.24B027
Abstract: In this paper, through the research of advantages and disadvantages of the particle swarm optimization algorithm, we get a new improved particle swarm optimization algorithm based on repulsion radius and repulsive factor. And a lot of test function experimental results show that the algorithm can effectively overcome the PSO algorithm precocious defect. PSO has significant improvement.
Particle Swarm Optimization Based Reactive Power Optimization  [PDF]
P. R. Sujin,T. Ruban Deva Prakash,M. Mary Linda
Computer Science , 2010,
Abstract: Reactive power plays an important role in supporting the real power transfer by maintaining voltage stability and system reliability. It is a critical element for a transmission operator to ensure the reliability of an electric system while minimizing the cost associated with it. The traditional objectives of reactive power dispatch are focused on the technical side of reactive support such as minimization of transmission losses. Reactive power cost compensation to a generator is based on the incurred cost of its reactive power contribution less the cost of its obligation to support the active power delivery. In this paper an efficient Particle Swarm Optimization (PSO) based reactive power optimization approach is presented. The optimal reactive power dispatch problem is a nonlinear optimization problem with several constraints. The objective of the proposed PSO is to minimize the total support cost from generators and reactive compensators. It is achieved by maintaining the whole system power loss as minimum thereby reducing cost allocation. The purpose of reactive power dispatch is to determine the proper amount and location of reactive support. Reactive Optimal Power Flow (ROPF) formulation is developed as an analysis tool and the validity of proposed method is examined using an IEEE-14 bus system.
Research of Particle Filter Based on Immune Particle Swarm Optimization  [PDF]
Long-Hua Ma,Yu Zhang,Zhe-Ming Lu,Hui Li
Information Technology Journal , 2013,
Abstract: Particle degradation, as a main limitation of particle filter, can be resolved by making use of common re-sampling method, but it always bring about the problem of sample dilution. The Immune Particle Swarm Optimization (IMPSO) was introduced into particle filter and a new kind of particle filter named IMPSO-based particle filter was proposed. In the IMPSO-based particle filter algorithm, particles are driven to the area with a higher posterior probability density and maintain big particle diversity at the same time. Simulation results show that IMPSO-based particle filter can eliminates the degeneracy phenomenon, avoid the sample dilution problem and guarantee the effectiveness.
An Improved Particle Swarm Optimization Algorithm based on Membrane Structure  [PDF]
Yanhua Zhong,Shuzhi Nie
International Journal of Computer Science Issues , 2013,
Abstract: Presented a new hybrid particle swarm algorithm based on P systems, through analyzing the working principle and improved strategy of the elementary particle swarm algorithm. Used the particles algorithm combined with the membrane to form a community, particles use wheel-type structure to communicate the current best particle within the community. The best particles, as Representative, compete for the optimal particle of the higher level. Utilized the Objective Functions to test the designed algorithm performance, compared with other particle swarm optimization algorithms, the experiment results shown that the designed algorithm has better performance in seeking Optimization solution quality, robustness and convergence speed.
Particle Swarm Optimization Based Direct Torque Control (Dtc) Of Induction Motor  [PDF]
International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering , 2013,
Abstract: This paper presents a new direct torque control (DTC) strategy for induction motor based on particle swarm optimization (PSO). In conventional direct torque controlled (DTC) induction motor drive, there is usually undesired torque and flux ripple. So Tuning PI parameters (Kp, Ki) are essential to DTC system to improve the performance of the system. In this work, particle swarm optimization (PSO) is proposed to adjust the parameters (Kp, Ki) of the speed controller in order to improve the performance of the system, and run the machine at reference speed.
Convergence Time Analysis of Particle Swarm Optimization Based on Particle Interaction  [PDF]
Chao-Hong Chen,Ying-ping Chen
Advances in Artificial Intelligence , 2011, DOI: 10.1155/2011/204750
Abstract: We analyze the convergence time of particle swarm optimization (PSO) on the facet of particle interaction. We firstly introduce a statistical interpretation of social-only PSO in order to capture the essence of particle interaction, which is one of the key mechanisms of PSO. We then use the statistical model to obtain theoretical results on the convergence time. Since the theoretical analysis is conducted on the social-only model of PSO, instead of on common models in practice, to verify the validity of our results, numerical experiments are executed on benchmark functions with a regular PSO program. 1. Introduction Particle swarm optimizer (PSO), introduced by [1, 2], is a stochastic population-based algorithm for solving continuous optimization problems. As shown by [3] and by lots of real-world applications, PSO is an efficient and effective optimization framework. Although PSO has been widely applied in many fields [4–7], understanding of PSO from the theoretical point of view is still quite limited. Most of previous theoretical results [8–18] are derived under the system that assumes a fixed attractor or a swarm consisting of a single particle. Due to the lack of theoretical analysis on PSO particle interaction, in this paper, we will make an attempt to analyze the convergence time for PSO on the facet of particle interaction. In particular, we will firstly introduce a statistical interpretation of PSO, proposed by [19], to capture the essence of particle interaction. We will then analyze the convergence time based on the statistical model. Finally, numerical experiments will be conducted to confirm the validity of our theoretical results obtained on simplified PSO, the social-only model, in a normal PSO configuration. In the next section, we will briefly introduce the algorithm of PSO and the statistical interpretation of social-only PSO. In Section 3, we will analyze the convergence time of PSO based on the statistical model. The experimental results are presented in Section 4, followed by Section 5 which concludes this work. 2. Particle Swarm Optimization and the Statistical Interpretation The social-only model of PSO can be described as pseudocode shown in Algorithm 1. In this paper, we will use boldface for vectors, for example, , . Without loss of generality, we assume that the goal is to minimize the objective function. Algorithm 1: Social-only model of PSO. According to Algorithm 1, in the beginning, particles are initialized, where is the swarm size, an algorithmic parameter of PSO. Each particle contains three types of information: its
Nonlinear Adaptive Filters based on Particle Swarm Optimization  [cached]
Leonardo Journal of Sciences , 2009,
Abstract: This paper presents a particle swarm optimization (PSO) algorithm to adjust the parameters of the nonlinear filter and to make this type of the filters more powerful for the elimination of the Gaussian noise and also the impulse noise. In this paper we apply the particle swarm optimization to the rational filters and we completed this work with the comparison between our results and other adaptive nonlinear filters like the LMS adaptive median filters and the no-adaptive rational filter.
2D Numerical Integration Method Based on Particle Swarm Optimization  [PDF]
Journal of Applied Computer Science & Mathematics , 2012,
Abstract: In this paper, a novel numerical double integration method based on Particle Swarm Optimization (PSO) was presented. PSO is a technique based on the cooperation between particles. The exchange of information between these particles allows to resolve difficult problems. This approach is carefully handled and tested with an illustrated example.
Particle Swarm Optimization (PSO) Based Turbine Control  [PDF]
Ali Tarique, Hossam A. Gabbar
Intelligent Control and Automation (ICA) , 2013, DOI: 10.4236/ica.2013.42018
Abstract: The steam turbine control system is strongly non-linear in all operating conditions. Proportional-Integral-Derivative (PID) controller that is currently used in control systems of many types of equipment is not considered highly precision for turbine speed control system. A fine tuning of the PID controller by some optimization technique is a desired objective to maintain the precise speed of the turbine in a wide range of operating conditions. This Paper evaluates the feasibility of the use of Particle Swarm Optimization (PSO) method for determining the optimal Proportional-Integral-Derivative (PID) controller parameters for steam turbine control. The turbine speed control is modelled in SimulinkTM with PID controller and the PSO algorithm is implemented in MATLAB to optimize the PID function. The PSO optimization technique is also compared with Genetic Algorithm (GA) and it is validated that PSO based controller is more efficient in reducing the steady-states error; settling time, rise time, and overshoot limit in speed control of the steam turbine control.
Cooperative Particle Swarm Optimization in Distance-Based Clustered Groups  [PDF]
Tomohiro Hayashida, Ichiro Nishizaki, Shinya Sekizaki, Shunsuke Koto
Journal of Software Engineering and Applications (JSEA) , 2017, DOI: 10.4236/jsea.2017.102008
Abstract: TCPSO (Two-swarm Cooperative Particle Swarm Optimization) has been proposed by Sun and Li in 2014. TCPSO divides the swarms into two groups with different migration rules, and it has higher performance for high-dimensional nonlinear optimization problems than traditional PSO and other modified method of PSO. This paper proposes a particle swarm optimization by modifying TCPSO to avoid inappropriate convergence onto local optima. The quite feature of the proposed method is that two kinds of subpopulations constructed based on the scheme of TCPSO are divided into some clusters based on distance measure, k-means clustering method, to maintain both diversity and centralization of search process are maintained. This paper conducts numerical experiments using several types of functions, and the experimental results indicate that the proposed method has higher performance than the TCPSO for large-scale optimization problems.
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