scholarly journals Control system research in wave compensation based on particle swarm optimization

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gang Tang ◽  
Peng Lu ◽  
Xiong Hu ◽  
Shaoyang Men
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Peak Time ◽  
Optimization Strategy ◽  
Swarm Optimization ◽  
Compensation Control ◽  
Swarm Algorithm

AbstractFor the offshore wave compensation control system, its controller setting will directly affect the platform's compensation effect. In order to study the wave compensation control system and optimization strategy, we build and simulate the wave compensation control model by using particle swarm optimization (PSO) to optimize the controller's control parameters and compare the results with other intelligent algorithms. Then we compare the response errors of the wave compensation platform under different PID controllers; and compare the particle swarm algorithm's response results and the genetic algorithm to the system controller optimization. The results show that the particle swarm algorithm is 63.94% lower than the genetic algorithm overshoot, and the peak time is 0.26 s lower, the adjustment time is 1.4 s lower than the genetic algorithm. It shows that the control effect of the wave compensation control system has a great relationship with the controller's parameter selection. Meanwhile, the particle swarm optimization algorithm's optimization can set the wave compensation PID control system, and it has the optimization effect of small overshoot and fast response time. This paper proposes the application of the particle swarm algorithm to the wave compensation system. It verifies the superiority of the method after application, and provides a new research reference for the subsequent research on the wave compensation control systems.

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

2021 ◽  
Author(s):  
Gui Zhou ◽  
Hang Wang ◽  
Minjun Peng
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Volume Control ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Basic Particle ◽  
Swarm Algorithm

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.

Optimization Technology of PID Parameter in Control System Based on Improved Particle Swarm Optimization Algorithms

2014 ◽  
Vol 908 ◽  
pp. 547-550
Author(s):  
Tian Shun Huang ◽  
Xiao Qiang Li ◽  
Hong Yun Lian ◽  
Zhi Qiang Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Mixed Integer ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Combinatorial Optimization Problems ◽  
Swarm Algorithm

Particle swarm algorithm has been proven to be very good solving many global optimization problems. Firstly we improved particle swarm optimization algorithm, the improved PSO algorithm for continuous optimization problem, in solving the nonlinear combinatorial optimization problems and mixed integer nonlinear optimization problems is very effective. This design adopts the improved particle swarm algorithm to optimize the PID parameters of the control system, and the effectiveness of the improved algorithm is proved by experiment.

Introduction of New Anti-dumping Algorithm in EU under WTO Law System Based on Particle Swarm Optimization

2021 ◽  
Vol 7 (5) ◽  
pp. 4558-4567
Author(s):  
Wenwen Deng
Keyword(s):  
Particle Swarm Optimization ◽  
Operation Mode ◽  
Particle Swarm ◽  
Algorithm Analysis ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Law System ◽  
Incomplete System ◽  
Swarm Algorithm ◽  
The Eu

Objectives: Anti dumping new algorithm is an innovative ability based on the WTO legal system, which has made an important contribution to the economic development of the EU system. Methods: At present, the operation mode of new antidumping algorithm has some defects, such as structure confusion and incomplete system implementation, which affects the development progress of EU economic growth. Results: Based on the above problems, in this paper, particle swarm algorithm is introduced, based on the optimization analysis of the website structure of the new antidumping algorithm, through the independent screening analysis of particle swarm optimization, combining the WTO economy with the EU status theory, Conclusion: the paper obtains the optimized anti-dumping innovation scheme on the basis of particle swarm algorithm analysis, and finally passes the input test. The feasibility of the scheme is established.

scholarly journals Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System

2018 ◽  
Vol 10 (12) ◽  
pp. 4445 ◽  
Author(s):  
Lejun Ma ◽  
Huan Wang ◽  
Baohong Lu ◽  
Changjun Qi
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Efficiency ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Operation ◽  
Computational Time ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Swarm Algorithm ◽  
Low Efficiency

In view of the low efficiency of the particle swarm algorithm under multiple constraints of reservoir optimal operation, this paper introduces a particle swarm algorithm based on strongly constrained space. In the process of particle optimization, the algorithm eliminates the infeasible region that violates the water balance in order to reduce the influence of the unfeasible region on the particle evolution. In order to verify the effectiveness of the algorithm, it is applied to the calculation of reservoir optimal operation. Finally, this method is compared with the calculation results of the dynamic programming (DP) and particle swarm optimization (PSO) algorithm. The results show that: (1) the average computational time of strongly constrained particle swarm optimization (SCPSO) can be thought of as the same as the PSO algorithm and lesser than the DP algorithm under similar optimal value; and (2) the SCPSO algorithm has good performance in terms of finding near-optimal solutions, computational efficiency, and stability of optimization results. SCPSO not only improves the efficiency of particle evolution, but also avoids excessive improvement and affects the computational efficiency of the algorithm, which provides a convenient way for particle swarm optimization in reservoir optimal operation.

Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization

2019 ◽  
Vol 13 (2) ◽  
pp. 18-29
Author(s):  
Rongrong Li ◽  
Linrun Qiu ◽  
Dongbo Zhang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Bottom Layer ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Elite Group ◽  
Searching Ability

In this article, a hierarchical cooperative algorithm based on the genetic algorithm and the particle swarm optimization is proposed that the paper should utilize the global searching ability of genetic algorithm and the fast convergence speed of particle swarm optimization. The proposed algorithm starts from Individual organizational structure of subgroups and takes full advantage of the merits of the particle swarm optimization algorithm and the genetic algorithm (HCGA-PSO). The algorithm uses a layered structure with two layers. The bottom layer is composed of a series of genetic algorithm by subgroup that contributes to the global searching ability of the algorithm. The upper layer is an elite group consisting of the best individuals of each subgroup and the particle swarm algorithm is used to perform precise local search. The experimental results demonstrate that the HCGA-PSO algorithm has better convergence and stronger continuous search capability, which makes it suitable for solving complex optimization problems.

A Novel Reactive Power Compensation Approach Based on Particle Swarm Optimization

2015 ◽  
Vol 740 ◽  
pp. 401-404
Author(s):  
Yun Zhi Li ◽  
Quan Yuan ◽  
Yang Zhao ◽  
Qian Hui Gang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Swarm Algorithm

The particle swarm optimization (PSO) algorithm as a stochastic search algorithm for solving reactive power optimization problem. The PSO algorithm converges too fast, easy access to local convergence, leading to convergence accuracy is not high, to study the particle swarm algorithm improvements. The establishment of a comprehensive consideration of the practical constraints and reactive power regulation means no power optimization mathematical model, a method using improved particle swarm algorithm for reactive power optimization problem, the algorithm weighting coefficients and inactive particles are two aspects to improve. Meanwhile segmented approach to particle swarm algorithm improved effectively address the shortcomings evolution into local optimum and search accuracy is poor, in order to determine the optimal reactive power optimization program.

scholarly journals A Simplified Recombinant PSO

2008 ◽  
Vol 2008 ◽  
pp. 1-10 ◽  
Author(s):  
Dan Bratton ◽  
Tim Blackwell
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Swarm Algorithm

Simplified forms of the particle swarm algorithm are very beneficial in contributing to understanding how a particle swarm optimization (PSO) swarm functions. One of these forms, PSO with discrete recombination, is extended and analyzed, demonstrating not just improvements in performance relative to a standard PSO algorithm, but also significantly different behavior, namely, a reduction in bursting patterns due to the removal of stochastic components from the update equations.

Clustering Algorithm Based on Improved Particle Swarm Algorithm

2013 ◽  
Vol 798-799 ◽  
pp. 689-692 ◽  
Author(s):  
Jin Hui Yang ◽  
Xi Cao
Keyword(s):  
Cluster Analysis ◽  
Particle Swarm Optimization ◽  
Local Search ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Initial Cluster ◽  
Cluster Analysis Method ◽  
Swarm Algorithm

K-means algorithm is a traditional cluster analysis method, has the characteristics of simple ideas and algorithms, and thus become one of the commonly used methods of cluster analysis. However, the K-means algorithm classification results are too dependent on the choice of the initial cluster centers for some initial value, the algorithm may converge in general suboptimal solutions. Analysis of the K-means algorithm and particle swarm optimization based on a clustering algorithm based on improved particle swarm algorithm. The algorithm local search ability of the K-means algorithm and the global search ability of particle swarm optimization, local search ability to improve the K-means algorithm to accelerate the convergence speed effectively prevent the occurrence of the phenomenon of precocious puberty. The experiments show that the clustering algorithm has better convergence effect.

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