Calculation of Jiles-Atherton hysteresis model's parameters using mix of chaos optimization algorithm and simulated annealing algorithm

2009 ◽  
Author(s):  
Huiqi Li ◽  
Qingfeng Li ◽  
Junjie Zhang
Keyword(s):  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Chaos Optimization ◽  
Chaos Optimization Algorithm ◽  
Annealing Algorithm

scholarly journals An Application of Simulated Annealing in Compensation of Nonlinearity of Scanners

2019 ◽  
Vol 9 (8) ◽  
pp. 1655 ◽  
Author(s):  
Rayyan Manwar ◽  
Mohsin Zafar ◽  
Adrian Podoleanu ◽  
Mohammad Avanaki
Keyword(s):  
Simulated Annealing ◽  
Response Time ◽  
Optimization Algorithm ◽  
Applied Voltage ◽  
Simulated Annealing Algorithm ◽  
Nonlinear Function ◽  
Signal Shape ◽  
Annealing Algorithm ◽  
Optimal Signal

Galvo scanners are popular devices for fast transversal scanning. A triangular signal is usually employed to drive galvo scanners at scanning rates close to the inverse of their response time where scanning deflection becomes a nonlinear function of applied voltage. To address this, the triangular signal is synthesized from several short ramps with different slopes. An optimization algorithm similar to a simulated annealing algorithm is used for finding the optimal signal shape to drive the galvo scanners. As a result, a significant reduction in the nonlinearity of the galvo scanning is obtained.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Wang ◽  
Yongqiang Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
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.

Light Ray Optimization Algorithm Based on Annealing Strategy

2012 ◽  
Vol 461 ◽  
pp. 435-439
Author(s):  
Ji Hong Shen ◽  
Jia Lian Li
Keyword(s):  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Simple Structure ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Intelligent Algorithm ◽  
Tuning Parameters ◽  
Hybrid Optimization Algorithm ◽  
Intelligent Optimization Algorithms ◽  
Annealing Algorithm

Based on Fermat’s principle, a new intelligent algorithm named light ray optimization was proposed for solving nonlinear optimization problems. The algorithm has the advantages of simple structure, few tuning parameters, and easily tuning. Compared with the other intelligent optimization algorithms, it has the strong ability to search globally, but poor local search ability. To solve this problem, simulated annealing strategy was introduced to light ray optimization, and a new hybrid optimization algorithm was put forward. The new hybrid algorithm enhances the ability of local searching of light ray optimization in the later searching period. The simulation results indicate that the new algorithm has better convergence probability and speed than light ray optimization, and the searching success rate of it is basically equal to that of simulated annealing algorithm.

Research on the Initial Value of the Simulated Annealing

2013 ◽  
Vol 774-776 ◽  
pp. 1770-1773
Author(s):  
Xiao Wen Liang ◽  
Wei Gong ◽  
Wen Long Fu ◽  
Jing Qi
Keyword(s):  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Hill Climbing ◽  
Local Optimum ◽  
Initial Value ◽  
Good Convergence ◽  
Annealing Algorithm

Simulated Annealing Algorithm is one of the top ten classical optimization algorithm, and it has been successfully applied to various fields. Simulated annealing is a optimization algorithm which can find the global optimal solution, compares to neural network algorithm, it is so easily to implement that has higher probability to be adopted, but it has own shortcomings like other optimization algorithms, its result largely depends on initial value, The initial value of the traditional simulated annealing algorithm began with a random number, its convergence speed is often slow very much and the effect is bad. In this paper, a new simulated annealing algorithm that based on genetic algorithm and hill-climbing method was brought up, because of hill-climbing algorithm was easy to fall into local optimum, and simulated annealing can just solve the problem, it not only escaped from local optimum, but also got good convergence speed and results.

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