Improvement of original particle swarm optimization algorithm based on simulated annealing algorithm

Author(s):  
Jihong Song ◽  
Wensuo Yi
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Wang ◽  
Yongqiang Liu

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.


2019 ◽  
Vol 118 ◽  
pp. 01038
Author(s):  
Shuyi Li ◽  
Xifeng Zhou ◽  
Qiangang Guo

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.


2011 ◽  
Vol 130-134 ◽  
pp. 3589-3594
Author(s):  
Bin Xiao ◽  
Zhao Hui Li

Through investigating the issue of solving the TSP problem by discrete particle swarm optimization algorithm, this study finds a new discrete particle swarm optimization algorithm (NDPSO), which is easy to combine with other algorithm and has fast convergence and high accuracy, by introducing the thought of the greedy algorithm and GA algorithm and refining the discrete particle swarm optimization algorithm. And then the study expands NDPSO by Simulated Annealing algorithm and proposes a hybrid discrete particle swarm optimization algorithm (HDPSO). At last, the experiments prove that these two algorithms both have good convergence, but the HDPSO has a better capacity to find the best solution.


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