Global Optimization for the Synthesis of Integrated Water Systems with Particle Swarm Optimization Algorithm

2008 ◽  
Vol 16 (1) ◽  
pp. 11-15 ◽  
Author(s):  
Yiqing LUO ◽  
Xigang UAN
2012 ◽  
Vol 512-515 ◽  
pp. 719-722
Author(s):  
Yan Ren ◽  
Yuan Zheng ◽  
Chong Li ◽  
Bing Zhou ◽  
Zhi Hao Mao

The hybrid wind/PV/pumped-storage power system was the hybrid system which combined hybrid wind/PV system and pumped-storage power station. System optimization was very important in the system design process. Particle swarm optimization algorithm was a stochastic global optimization algorithm with good convergence and high accuracy, so it was used to optimize the hybrid system in this paper. First, the system reliability model was established. Second, the particle swarm optimization algorithm was used to optimize the system model in Nanjing. Finally, The results were analyzed and discussed. The optimization results showed that the optimal design method of wind/PV/pumped-storage system based on particle swarm optimization could take into account both the local optimization and the global optimization, which has good convergence high precision. The optimal system was that LPSP (loss of power supply probability) was zero.


2014 ◽  
Vol 937 ◽  
pp. 548-553
Author(s):  
Shu Hui Chang ◽  
Zhi Hong Qie

In this paper, Stochastic Particle Swarm Optimization Algorithm (SPSO) is introduced to calculate parameters of crop water production function directly. SPSO is an improvement of the standard PSO, compared with which, it can be guaranteed to converge to the global optimization solution with probability one in theory, and speed up the convergence. The dimension of space of a particulate is determined by the phases of crop growth and development period in SPSO, and the sensitive index , an important parameter of crop water production function, is regarded as optimized variable to be calculated directly. This method can eliminate some problems existing in linear regression method, such as biased estimate and distortion of result and it can search the global optimization value more easily than the PSO. Through example analysis, we can see that the SPSO is better than PSO in accuracy and calculation speed.


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