Adaptive Noise Canceller Design Based on Chaotic Simulated Annealing Particle Swarm Optimization 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.

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An improved particle swarm optimization algorithm based on simulated annealing

2014 10th International Conference on Natural Computation (ICNC) ◽

10.1109/icnc.2014.6975891 ◽

2014 ◽

Cited By ~ 1

Author(s):

Huafen Yang ◽

You Yang ◽

Zuyuan Yang ◽

Lihui Zhang

Keyword(s):

Particle Swarm Optimization ◽

Simulated Annealing ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization

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Hybrid Particle Swarm Optimization Algorithm Merging Simulated Annealing and Mountain-Climb Searching

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.651-653.2159 ◽

2014 ◽

Vol 651-653 ◽

pp. 2159-2163

Author(s):

Jia Xing You ◽

Ji Li Chen ◽

Ming Gang Dong

Keyword(s):

Particle Swarm Optimization ◽

Simulated Annealing ◽

Local Search ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Premature Convergence ◽

Swarm Optimization ◽

Hybrid Particle ◽

Hybrid Particle Swarm Optimization

To solve the problem of standard particle swarm optimization (PSO) easy turn to premature convergence and poor ability in local search, this paper present a hybrid particle swarm optimization algorithm merging simulated annealing (SA) and mountain-climb. During the running time, the algorithm use the pso to find the global optimal position quickly, take advantage of the Gaussian mutation and mountain-climb strategy to enhance local search ability, and combine with SA to strengthen the population diversity to enable particles to escape from local minima. Test results on several typical test functions show that this new algorithm has a significant improve in searching ability and effectively overcome the premature convergence problem.

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Application of Simulated Annealing Particle Swarm Optimization in Response Spectrum Fitting of Simulated Earthquake Wave

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.444-445.1082 ◽

2013 ◽

Vol 444-445 ◽

pp. 1082-1086

Author(s):

Xue Ni Wang ◽

Jing Zhou

Keyword(s):

Particle Swarm Optimization ◽

Simulated Annealing ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Response Spectrum ◽

Particle Swarm ◽

Swarm Optimization ◽

Fitting Method ◽

Design Response Spectrum ◽

Spectrum Fitting

In order to get a simulated earthquake wave whose response spectrum fitted well to the smooth design response spectrum, a model was established by making the standard error between the response spectrum of simulated earthquake wave and the design response spectrum as the minimal optimization objective. Simulated annealing particle swarm optimization algorithm, which was an improvement algorithm of particle swarm optimization, was used to solve the model. This spectrum fitting method was compared with the conventional spectrum fitting method, which adjusted Fourier amplitude spectrum in frequency domain. The results show that the method of response spectrum fitting by applying simulated annealing particle swarm optimization algorithm has a good convergence. And the response spectrum of simulated earthquake wave generated by simulated annealing particle swarm optimization algorithm agrees better with the design response spectrum than that by conventional spectrum fitting method.

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