Research on Optimization and Verification Method of Sensor Arrangement in the Chemical and Volume Control System

Control System ◽

Optimization Algorithm ◽

Particle Swarm ◽

Volume Control ◽

Swarm Optimization ◽

Basic Particle ◽

Abstract In order to avoid the nuclear accidents during the operation of nuclear power plants, it is necessary to always monitor the status of relevant facilities and equipment. The premise of condition monitoring is that the sensor can provide sufficient and accurate operating parameters. Therefore, the sensor arrangement must be rationalized. As one of the nuclear auxiliary systems, the chemical and volume control system plays an important role in ensuring the safe operation of nuclear power plants. There are plenty of sensor measuring points arranged in the chemical and volume control system. These sensors are not only for detecting faults, but also for running and controlling services. Particle swarm algorithm has many applications in solving the problem of sensor layout optimization but the disadvantage of the basic particle swarm optimization algorithm is that the parameters are fixed, the particles are single, and it is easy to fall into the local optimization. In this paper, the basic particle swarm optimization algorithm is improved by Non-linearly adjusting inertia weight factor, asynchronously changing learning factor, and variating particle. The improved particle swarm optimization algorithm is used to optimize the sensor placement. The numerical analysis verified that a smaller number of sensors can meet the fault detection requirements of the chemical and volume control system in this paper, and Experiments have proved that the improved particle swarm algorithm can improve the basic particle swarm algorithm, which is easy to fall into the shortcomings of local optimization and single particles. This method has good applicability, and could be also used to optimize other systems with sufficient parameters and consistent objective function.

A Local and Global Search Combined Particle Swarm Optimization Algorithm and Its Convergence Analysis

Mathematical Problems in Engineering ◽

10.1155/2014/905712 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Weitian Lin ◽

Zhigang Lian ◽

Xingsheng Gu ◽

Bin Jiao

Keyword(s):

Optimization Algorithm ◽

Particle Swarm ◽

Global Search ◽

Fine Tuning ◽

Swarm Optimization ◽

Large Size ◽

Particle swarm optimization algorithm (PSOA) is an advantage optimization tool. However, it has a tendency to get stuck in a near optimal solution especially for middle and large size problems and it is difficult to improve solution accuracy by fine-tuning parameters. According to the insufficiency, this paper researches the local and global search combine particle swarm algorithm (LGSCPSOA), and its convergence and obtains its convergence qualification. At the same time, it is tested with a set of 8 benchmark continuous functions and compared their optimization results with original particle swarm algorithm (OPSOA). Experimental results indicate that the LGSCPSOA improves the search performance especially on the middle and large size benchmark functions significantly.

An Improved Particle Swarm Optimization Based on Biological Chemotaxis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1015.737 ◽

2014 ◽

Vol 1015 ◽

pp. 737-740

Author(s):

Hui Xia

Keyword(s):

Optimization Algorithm ◽

Particle Swarm ◽

Function Optimization ◽

Swarm Optimization ◽

Multimodal Function ◽

Swarm Algorithm ◽

Improved Algorithm

Standard particle swarm algorithm for function optimization prone to local optimal and premature convergence, and thus the biological chemotaxis principle introduction to particle swarm optimization algorithm, this paper proposed an improved algorithm to maintain the diversity of the populationand the choice of key parameters. Simulation results show that, compared with the traditional particle swarm optimization algorithm, an improved particle swarm algorithm for dealing with complex multimodal function optimization problem can be significantly improved algorithm for global optimization.

A K-Medoids Clustering Algorithm Based on Particle Swarm Algorithm with Simulated Annealing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.325-326.1628 ◽

2013 ◽

Vol 325-326 ◽

pp. 1628-1631 ◽

Cited By ~ 1

Author(s):

Hong Zhou ◽

Ke Luo

Keyword(s):

Simulated Annealing ◽

Optimization Algorithm ◽

Clustering Algorithm ◽

Particle Swarm ◽

Swarm Optimization ◽

Optimal Value ◽

Be aimed at the problems that K-medoids algorithm is easy to fall into the local optimal value and basic particle swarm algorithm is easy to fall into the premature convergence, this paper joins the Simulated Annealing (SA) thought and proposes a novel K-medoids clustering algorithm based on Particle swarm optimization algorithm with simulated annealing. The new algorithm combines the quick optimization ability of particle swarm optimization algorithm and the probability of jumping property with SA, and maintains the characteristics that particle swarm algorithm is easy to realize, and improves the ability of the algorithm from local extreme value point. The experimental results show that the algorithm enhances the convergence speed and accuracy of the algorithm, and the clustering effect is better than the original k-medoids algorithm.

Multi-Objective Optimization Design for Gear Reducer Based on the Grey Particle Swarm Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.631-632.1044 ◽

2013 ◽

Vol 631-632 ◽

pp. 1044-1050

Author(s):

Feng An ◽

Si Cong Yuan ◽

Wei Dong Yan ◽

Dong Hong Wang

Keyword(s):

Optimization Algorithm ◽

Particle Swarm ◽

Extreme Value ◽

Original Design ◽

Swarm Optimization ◽

Correlation Degree ◽

Combining the thought of correlation degree analysis in the theory of grey, use of particle swarm algorithm, seeking it’s individual extreme value and global extreme value, and puts forward to the goal of mathematical model about more gray particle swarm optimization algorithm is presented, the algorithm is applied to speed reducer hoisting mechanism in the optimization of parameters. The optimization results show that the optimal parameters, than the original design of parameters for satisfactory results show the particle swarm optimization algorithm is used for gray hoisting mechanism optimized parameter design of gear reducer is effective and feasible.

Particle Swarm Optimization Algorithm in Improved Electrical Control System

Journal of Physics Conference Series ◽

10.1088/1742-6596/2066/1/012020 ◽

2021 ◽

Vol 2066 (1) ◽

pp. 012020

Author(s):

Xiaotao Tian

Keyword(s):

Control System ◽

Control Systems ◽

Optimization Algorithm ◽

Particle Swarm ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Electrical Control

Abstract In today’s social background where high-tech emerges endlessly, various production fields in our country have fully entered the era of mechanical automation and electrical automation, and electrical control systems have been widely used in our country’s electrical appliance manufacturing industry. This paper is based on the theoretical analysis of the particle swarm optimization algorithm. Based on this optimization algorithm, a brand-new particle swarm optimization algorithm is obtained. It is applied to the electrical control system to improve it and makes full use of the improved particle swarm optimization algorithm. The existing electrical control system is optimized. This article firstly analyzes the types of common electrical control systems, puts forward some basic methods to improve the control system, and then explains the effective techniques for improvement, hoping to make reference to the improvement of electrical control systems later in this article. This article first improves the particle swarm optimization algorithm, adding the ability to adjust the control system and dynamic learning factors, focusing on strengthening the later stage of the optimization of the particle swarm algorithm and the ability to converge to improve the efficiency of the calculation. The second is to improve the traditional particle swarm optimization algorithm and update the calculation method of the formula to reduce the possibility of selecting undesirable particles and affecting the optimization results. Finally, through MATLAB and reverse simulation analysis, compared with the traditional electrical control system algorithm, the improved particle swarm optimization algorithm has a faster convergence speed and high control system efficiency. The experimental research results show that the particle swarm optimization algorithm proposed in this paper has a huge advantage compared with other algorithms, and its parameter optimization gives full play to the powerful performance of the electrical control system.

Quantum Behaved Particle Swarm Optimization Algorithm Based on Artificial Fish Swarm

Mathematical Problems in Engineering ◽

10.1155/2014/592682 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 10

Author(s):

Dong Yumin ◽

Zhao Li

Keyword(s):

Optimization Algorithm ◽

Particle Swarm ◽

Local Extremum ◽

Swarm Optimization ◽

Adaptive Parameters ◽

Artificial Fish Swarm ◽

Quantum behaved particle swarm algorithm is a new intelligent optimization algorithm; the algorithm has less parameters and is easily implemented. In view of the existing quantum behaved particle swarm optimization algorithm for the premature convergence problem, put forward a quantum particle swarm optimization algorithm based on artificial fish swarm. The new algorithm based on quantum behaved particle swarm algorithm, introducing the swarm and following activities, meanwhile using the adaptive parameters, to avoid it falling into local extremum of population. The experimental results show the improved algorithm to improve the optimization ability of the algorithm.

USE OF PARTICLE SWARM OPTIMIZATION ALGORITHM FOR DIGITALIZED SINEWAVE SIGNAL PARAMETERS ESTIMATION

International Journal of Computing ◽

10.47839/ijc.16.4.908 ◽

2017 ◽

pp. 203-209

Author(s):

Pierfrancesco Raimondo

Keyword(s):

Optimization Algorithm ◽

Classical Method ◽

Particle Swarm ◽

Parameters Estimation ◽

Swarm Optimization ◽

Fitting Algorithm ◽

In the paper is proposed a procedure based on the particle swarm optimization algorithm for parameters estimation of sinewave signals as: amplitude, phase, frequency and offset. Differently from the classical method used to solve this problem (the sine-fitting algorithms), the proposed procedure considers the estimation problem as an optimization one. In fact, the particle swarm algorithm tends to global solution instead of a local solution. The proposed procedure preliminarily estimates the raw value of the parameters under investigation by a time analysis of the input signal. Successively, these values are used by the particle swarm algorithm for the final estimation result. The tests of the proposed procedure determine the most effective cost function for the algorithm and confirm that the achievable performances are in according with the sine fitting algorithm. Moreover, the execution time for the proposed procedure is lower than the sine fitting, making it an interesting alternative.

Application of Simulated Annealing Particle Swarm Optimization Based on Correlation in Parameter Identification of Induction Motor

Mathematical Problems in Engineering ◽

10.1155/2018/1869232 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Lei Wang ◽

Yongqiang Liu

Keyword(s):

Simulated Annealing ◽

Optimization Algorithm ◽

Simulated Annealing Algorithm ◽

Particle Swarm ◽

Swarm Optimization ◽

Correlation Algorithm ◽

Annealing Algorithm

The strengths and weaknesses of correlation algorithm, simulated annealing algorithm, and particle swarm optimization algorithm are studied in this paper. A hybrid optimization algorithm is proposed by drawing upon the three algorithms, and the specific application processes are given. To extract the current fundamental signal, the correlation algorithm is used. To identify the motor dynamic parameter, the filtered stator current signal is simulated using simulated annealing particle swarm algorithm. The simulated annealing particle swarm optimization algorithm effectively incorporates the global optimization ability of simulated annealing algorithm with the fast convergence of particle swarm optimization by comparing the identification results of asynchronous motor with constant torque load and step load.