A Hybrid Global Optimization Algorithm: Particle Swarm Optimization in Association with a Genetic Algorithm

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.

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Particle Swarm Optimization Algorithm vs. Genetic Algorithm to Solve Multi-Objective Optimization Problem in Gait Planning of Biped Robot

Advances in Intelligent and Soft Computing - Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012 ◽

10.1007/978-3-642-27443-5_65 ◽

2012 ◽

pp. 563-570 ◽

Cited By ~ 1

Author(s):

Rega Rajendra ◽

Dilip Kumar Pratihar

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Optimization Problem ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Biped Robot ◽

Multi Objective Optimization ◽

Swarm Optimization ◽

Gait Planning

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Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Energies ◽

10.3390/en13112873 ◽

2020 ◽

Vol 13 (11) ◽

pp. 2873 ◽

Cited By ~ 1

Author(s):

Dinh Thanh Viet ◽

Vo Van Phuong ◽

Minh Quan Duong ◽

Quoc Tuan Tran

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Wind Power ◽

Optimization Algorithm ◽

Electricity Market ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Short Term ◽

Swarm Optimization

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

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Metropolis Particle Swarm Optimization Algorithm with Mutation Operator for Global Optimization Problems

2010 22nd IEEE International Conference on Tools with Artificial Intelligence ◽

10.1109/ictai.2010.15 ◽

2010 ◽

Cited By ~ 4

Author(s):

L. Idoumghar ◽

M. Idrissi-Aouad ◽

M. Melkemi ◽

R. Schott

Keyword(s):

Global Optimization ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Optimization Problems ◽

Particle Swarm ◽

Mutation Operator ◽

Swarm Optimization

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Ply stacking sequence optimization of composite driveshaft using particle swarm optimization algorithm

International Journal for Simulation and Multidisciplinary Design Optimization ◽

10.1051/smdo/2013001 ◽

2014 ◽

Vol 5 ◽

pp. A16 ◽

Cited By ~ 2

Author(s):

K. Manjunath ◽

T. Rangaswamy

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Epoxy Composite ◽

Particle Swarm ◽

Stacking Sequence ◽

Swarm Optimization ◽

Stacking Sequence Optimization ◽

Single Piece

In this paper an attempt has been made to optimize ply stacking sequence of single piece E-Glass/Epoxy, HM Carbon/Epoxy and Boron/Epoxy composite drive shafts using particle swarm optimization (PSOA). PSOA programme is developed using MATLAB V 7 to optimize the ply stacking sequence with an objective of weight minimization. The weight savings of the E-Glass/Epoxy, HM Carbon/Epoxy and Boron/Epoxy shaft are 51%, 87% and 85% of the steel shaft respectively. The optimum results of PSOA obtained are compared with results of genetic algorithm (GA) and found that PSOA yields better results than GA.

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