scholarly journals PI Controller of Speed Regulation of Brushless DC Motor Based on Particle Swarm Optimization Algorithm with Improved Inertia Weights

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Xie ◽  
Jie-Sheng Wang ◽  
Hai-Bo Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Dc Motor ◽  
Pi Controller ◽  
Brushless Dc Motor ◽  
Swarm Optimization ◽  
Brushless Dc ◽  
Inertia Weight ◽  
Speed Regulation

The brushless director current (DC) motor is a new type of mechatronic motor that has been developed rapidly with the development of power electronics technology and the emergence of new permanent magnet materials. Based on the speed regulation characteristics, speed regulation strategy, and mathematical model of brushless DC motor, a parameter optimization method of proportional-integral (PI) controller on speed regulation for the brushless DC motor based on particle swarm optimization (PSO) algorithm with variable inertia weights is proposed. The parameters of PI controller are optimized by PSO algorithm with five inertia weight adjustment strategies (linear descending inertia weight, linear differential descending inertia weight, incremental-decremented inertia weight, nonlinear descending inertia weight with threshold, and nonlinear descending inertia weight with control factor). The effectiveness of the proposed method is verified by the simulation experiments and the related simulation results.

An Improved Particle Swarm Optimization Algorithm with Adaptive Inertia Weights

2019 ◽  
Vol 18 (03) ◽  
pp. 833-866 ◽  
Author(s):  
Mi Li ◽  
Huan Chen ◽  
Xiaodong Wang ◽  
Ning Zhong ◽  
Shengfu Lu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Random Mutation ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Fitness Value ◽  
The One ◽  
Individual Particles

The particle swarm optimization (PSO) algorithm is simple to implement and converges quickly, but it easily falls into a local optimum; on the one hand, it lacks the ability to balance global exploration and local exploitation of the population, and on the other hand, the population lacks diversity. To solve these problems, this paper proposes an improved adaptive inertia weight particle swarm optimization (AIWPSO) algorithm. The AIWPSO algorithm includes two strategies: (1) An inertia weight adjustment method based on the optimal fitness value of individual particles is proposed, so that different particles have different inertia weights. This method increases the diversity of inertia weights and is conducive to balancing the capabilities of global exploration and local exploitation. (2) A mutation threshold is used to determine which particles need to be mutated. This method compensates for the inaccuracy of random mutation, effectively increasing the diversity of the population. To evaluate the performance of the proposed AIWPSO algorithm, benchmark functions are used for testing. The results show that AIWPSO achieves satisfactory results compared with those of other PSO algorithms. This outcome shows that the AIWPSO algorithm is conducive to balancing the abilities of the global exploration and local exploitation of the population, while increasing the diversity of the population, thereby significantly improving the optimization ability of the PSO algorithm.

scholarly journals On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Martins Akugbe Arasomwan ◽  
Aderemi Oluyinka Adewumi
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Terminal Point ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Inertia Weight

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted.

Particle Swarm Optimization with Team Spirit Inertia Weight

2011 ◽  
Vol 383-390 ◽  
pp. 5744-5750
Author(s):  
Xi Zhen Wang ◽  
Yan Li ◽  
Gang Hu Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Convergence Problem ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Standard Particle Swarm Optimization ◽  
Decline Curves ◽  
Team Spirit ◽  
Global And Local

A PSO Algorithm with Team Spirit Inertia weight (TSWPSO) is presented based on the study of the effect of inertia weight on Standard Particle Swarm Optimization (SPSO). Due to the theory of group in organization psychology, swarm is divided into multiple sub-swarms and search is run in a number of different sub-swarms which are parallel performed. Try to find or modify a curve which is compatible with optimized object within many inertia weight decline curves, in order to balance the global and local explorations ability in particle swarm optimization and to avoid the premature convergence problem effectively. The testes by five classical functions show that, TSWPSO has a better performance in both the convergence rate and the precision.

A Particle Swarm Optimization with Variety Factor

2012 ◽  
Vol 239-240 ◽  
pp. 1291-1297 ◽  
Author(s):  
Hai Sheng Qin ◽  
Deng Yue Wei ◽  
Jun Hui Li ◽  
Lei Zhang ◽  
Yan Qiang Feng
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Property ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Premature Convergence ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Previous Algorithm ◽  
Modified Pso

A new particle swarm optimization (PSO) algorithm (a PSO with Variety Factor, VFPSO) based on the PSO was proposed. Compared with the previous algorithm, the proposed algorithm is to update the Variety Factor and to improve the inertia weight of the PSO. The target of the improvement is that the new algorithm could go on enhancing the robustness as before and should reduce the risk of premature convergence. The simulation experiments show that it has great advantages of convergence property over some other modified PSO algorithms, and also avoids algorithm being trapped in local minimum effectively. So it can avoid the phenomenon of premature convergence.

scholarly journals Refined Heuristic Swarm Intelligence Algorithm

2020 ◽  
pp. 267-275
Author(s):  
I. I. Aina ◽  
C. N. Ejieji
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

In this paper, a new metaheuristic algorithm named refined heuristic intelligence swarm (RHIS) algorithm is developed from an existing particle swarm optimization (PSO) algorithm by introducing a disturbing term to the velocity of PSO and modifying the inertia weight, in which the comparison between the two algorithms is also addressed.

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