An anycast routing algorithm based on the combination of particle swarm optimization algorithm and genetic algorithm

2014 ◽  
Author(s):  
Taoshen Li ◽  
Zhihui Ge ◽  
Qin Xiong
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Routing Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Anycast Routing

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

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2873 ◽  
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.

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

2014 ◽  
Vol 5 ◽  
pp. A16 ◽  
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.

Asymmetric Bouc–Wen hysteresis modeling and inverse compensation for piezoelectric actuator via a genetic algorithm–based particle swarm optimization identification algorithm

2019 ◽  
Vol 30 (8) ◽  
pp. 1263-1275 ◽  
Author(s):  
Quan Zhang ◽  
Yichong Dong ◽  
Yan Peng ◽  
Jun Luo ◽  
Shaorong Xie ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Piezoelectric Ceramic ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Independent Particle ◽  
Hysteresis Characteristics

The hysteresis characteristics, which commonly existed in smart materials–based actuators, play a significant role in precision control technology. In this article, a modified Bouc–Wen model which can describe the asymmetric hysteresis characteristics of piezoelectric ceramic actuators is investigated. The corresponding parameters of the modified Bouc–Wen hysteresis model are identified through a genetic algorithm–based particle swarm optimization algorithm. Compared with independent particle swarm optimization method which is easily trapped in the local extremum, the proposed genetic algorithm–based particle swarm optimization features the strong searching ability both in early global search period and the later local search period. The experimental results show that the asymmetric Bouc–Wen model identified via genetic algorithm–based particle swarm optimization algorithm are more accurate than that identified through independent particle swarm optimization or genetic algorithm approach, and the maximum displacement error and the maximum relative error between the genetic algorithm–based particle swarm optimization model and the experimental value are 0.20 µm and 14.28%, respectively, which are much smaller than that of particle swarm optimization method with 0.67 µm and 47.85% and genetic algorithm method with 0.35 µm and 25%. In order to further verify the accuracy of the identified model, the hysteresis compensation of piezoelectric ceramic actuator was realized using the feedforward controller based on the inverse Bouc–Wen model.

B-Spline Curve Fitting Based on Adaptive Particle Swarm Optimization Algorithm

2010 ◽  
Vol 20-23 ◽  
pp. 1299-1304 ◽  
Author(s):  
Yue Hong Sun ◽  
Zhao Ling Tao ◽  
Jian Xiang Wei ◽  
De Shen Xia
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Curve Fitting ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Control Points ◽  
Swarm Optimization ◽  
B Spline ◽  
Spline Curve

For fitting of ordered plane data by B-spline curve with the least squares, the genetic algorithm is generally used, accompanying the optimization on both the data parameter values and the knots to result in good robust, but easy to fall into local optimum, and without improved fitting precision by increasing the control points of the curve. So what we have done are: combining the particle swarm optimization algorithm into the B-spline curve fitting, taking full advantage of the distribution characteristic for the data, associating the data parameters with the knots, coding simultaneously the ordered data parameter and the number of the control points of the B-spline curve, proposing a new fitness function, dynamically adjusting the number of the control points for the B-spline curve. Experiments show the proposed particle swarm optimization method is able to adaptively reach the optimum curve much faster with much better accuracy accompanied less control points and less evolution times than the genetic algorithm.

Vehicle Path Planning Method Based on Particle Swarm Optimization Algorithm

2012 ◽  
Vol 468-471 ◽  
pp. 2745-2748
Author(s):  
Sheng Long Yu ◽  
Yu Ming Bo ◽  
Zhi Min Chen ◽  
Kai Zhu
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Adaptive Genetic Algorithm ◽  
Swarm Optimization ◽  
Vehicle Path

A particle swarm optimization algorithm (PSO) is presented for vehicle path planning in the paper. Particle swarm optimization proposed by Kennedy and Eberhart is derived from the social behavior of the birds foraging. Particle swarm optimization algorithm a kind of swarm-based optimization method.The simulation experiments performed in this study show the better vehicle path planning ability of PSO than that of adaptive genetic algorithm and genetic algorithm. The experimental results show that the vehicle path planning by using PSO algorithm has the least cost and it is indicated that PSO algorithm has more excellent vehicle path planning ability than adaptive genetic algorithm,genetic algorithm.

scholarly journals Binary Particle Swarm Optimization Algorithm for Kidney Exchanges

2020 ◽  
Author(s):  
Wael M. F. Abdel-Rehim
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Kidney Exchange ◽  
To Receive

In this paper, we implement a Particle Swarm Optimization (PSO) based method for solving the kidney exchange problem. Because using a kidney exchange, we can help incompatible patient-donor couples to swap donors to receive a compatible kidney. Implementing Particle Swarm Optimization in solving the kidney exchange problem is an effective method because the obtained results indicate that PSO outperforms other stochastic-based methods such as Genetic Algorithm regarding the efficient the number of resulting exchanges.

scholarly journals A Microstrip Antenna Design Using an Heuristic Algorithm

2020 ◽  
Vol 4 (1) ◽  
pp. 25
Author(s):  
I Made Adhi Wiryawan ◽  
Maria Veronica Astrid Wahyuningtyas ◽  
Anugerah Galang Persada ◽  
Dyonisius Dony Ariananda
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Return Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Algorithm Design ◽  
Particle Swarm ◽  
Microstrip Antennas ◽  
Swarm Optimization

Microstrip antennas have several advantages. Some of them are that they have a compact shape and small dimensions. Moreover, they are also easy to be fabricated and easily connected as well as integrated with other electronic devices. Currently, designing antennas conventionally is limited by time, energy, and experience as well as expertise. As an alternative, a way to design antennas with revolutionary methods is developed using algorithms and computing. Algorithm design techniques can overcome limitations and automatically find practical solutions that usually take a long time to discover. The particle swarm optimization algorithm and a genetic algorithm can find solutions from microstrip antennas. Objective functions play an essential role in heuristic algorithms. With a proper objective function, simulation results are obtained on the particle swarm optimization algorithm with a return loss value of -47.837, VSWR of 1.0083, and impedance of 46.805 Ω. In contrast, the genetic algorithm obtains return loss of -16.157 dB, impedance of 50.233 Ω, and VSWR of 1.3687.

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