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

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.

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.

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

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.

scholarly journals Research on Microgrid Optimization Based on Simulated Annealing Particle Swarm Optimization

2019 ◽  
Vol 118 ◽  
pp. 01038
Author(s):  
Shuyi Li ◽  
Xifeng Zhou ◽  
Qiangang Guo
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Storage Device ◽  
Swarm Optimization

Based on the pursuit of different goals in the operation of the microgrid, it is not possible to meet the lowest cost and the least pollution at the same time. From the perspective of economy and environmental protection, a microgrid model including photovoltaic power generation, wind power generation, micro gas turbine, fuel cell and energy storage device is proposed. This paper establishes a comprehensive benefit objective function that considers both microgrid fuel cost, maintenance management cost, depreciation cost, interaction cost with public grid and pollutant treatment cost. In order to avoid the defect that the traditional particle swarm optimization algorithm is easy to fall into the local optimal solution, this paper uses the combination of simulated annealing algorithm and particle swarm optimization algorithm to compare with the traditional particle swarm optimization algorithm to obtain a more suitable method for microgrid operation. Finally, a typical microgrid in China is taken as an example to verify the feasibility of the algorithm.

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