Optimum Design of Arch Dams using a Combination of Simultaneous Perturbation Stochastic Approximation and Genetic Algorithms

2009 ◽  
Author(s):  
J. Salajegheh ◽  
E. Salajegheh ◽  
S.M. Seyedpoor ◽  
S. Gholizadeh
Keyword(s):  
Genetic Algorithms ◽  
Optimum Design ◽  
Stochastic Approximation ◽  
Simultaneous Perturbation Stochastic Approximation ◽  
Arch Dams

Optimum Shape Design of Arch Dams by a Combination of Simultaneous Perturbation Stochastic Approximation and Genetic Algorithm Methods

2008 ◽  
Vol 11 (5) ◽  
pp. 501-510 ◽  
Author(s):  
S. M. Seyedpoor ◽  
S. Gholizadeh
Keyword(s):  
Genetic Algorithm ◽  
Optimum Design ◽  
Stochastic Approximation ◽  
Computational Cost ◽  
Optimum Shape ◽  
Shape Design ◽  
Initial Population ◽  
Second Phase ◽  
Simultaneous Perturbation Stochastic Approximation ◽  
Arch Dams

An efficient method is proposed to find optimal shape of arch dams subjected to response spectrum loading. The optimization is performed by a combination of simultaneous perturbation stochastic approximation (SPSA) and genetic algorithm (GA) methods. This new method is called simultaneous perturbation genetic algorithm (SPGA). Operation of SPGA includes three phases. In the first phase, a preliminary optimization is accomplished using SPSA. In the second phase, an optimal initial population is produced using the first phase results. In the last phase, GA is employed to find optimum design using the optimal initial population. The numerical results reveal the robustness and high performance of the proposed method for optimum shape design of arch dams. The optimum design obtained by SPGA is compared with those of SPSA and GA. It is demonstrated that the SPGA converges to better solution compared to SPSA and GA by spending lower computational cost.

Optimum design method for four-bar function generator using AIS and genetic algorithms

2013 ◽  
Author(s):  
Morteza Saeidi Javash ◽  
Mir Mohammad Ettefagh ◽  
Yousof Ebneddin Hamidi
Keyword(s):  
Genetic Algorithms ◽  
Optimum Design ◽  
Design Method ◽  
Function Generator

Maximum Power Point Tracking of Multi-String Photovoltaic Array via Simultaneous Perturbation Stochastic Approximation

2013 ◽  
Author(s):  
Yan Xiao ◽  
Yaoyu Li ◽  
John E. Seem ◽  
Kaushik Rajashekara
Keyword(s):  
Stochastic Approximation ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Pv System ◽  
Maximum Power Point ◽  
Simultaneous Perturbation Stochastic Approximation ◽  
Pv Systems ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

This paper presents a Maximum Power Point Tracking (MPPT) strategy for multi-string photovoltaic (PV) systems using the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. The multi-string PV system considered is a decentralized control configuration, controlling the voltage reference to each PV module but based on the feedback of the total power at the DC bus. This requires only one pair of voltage and current measurements. The MPPT control problem for such topology of multi-string PV systems features a high input dimension, which can dramatically slow down the searching process for the real-time optimization process involved. The SPSA algorithm is considered in this study due to its remarkable capability of fast convergence for high dimensional search problems endorsed by various applications recently. Simulation study is performed for an 8-string PV system, and experimental study is performed for a 4-string PV system. Good performances are observed for both simulation and experimental results.

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