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.

Analysis of FBG Reflection Spectrum with PSO Algorithm

2012 ◽  
Vol 182-183 ◽  
pp. 1953-1957
Author(s):  
Zhao Xia Wu ◽  
Shu Qiang Chen ◽  
Jun Wei Wang ◽  
Li Fu Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
High Precision Measurement ◽  
Swarm Optimization ◽  
Fbg Sensor ◽  
Fast Convergence Rate ◽  
Force Sense

When the parameters were measured by using fiber Bragg grating (FBG) in practice, there were some parameters hard to measure, which would influenced the reflective spectral of FBG severely, and make the characteristic information harder to be extracted. Therefore, particle swarm optimization algorithm was proposed in analyzing the uniform force reflective spectral of FBG. Based on the uniform force sense theory of FBG and particle swarm optimization algorithm, the objective function were established, meanwhile the experiment and simulation were constructed. And the characteristic information in reflective spectrum of FBG was extracted. By using particle swarm optimization algorithm, experimental data showed that particle swarm optimization algorithm used in extracting the characteristic information not only was efficaciously and easily, but also had some advantages, such as high accuracy, stability and fast convergence rate. And it was useful in high precision measurement of FBG sensor.

scholarly journals A Heuristic Elastic Particle Swarm Optimization Algorithm for Robot Path Planning

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 99 ◽  
Author(s):  
Haiyan Wang ◽  
Zhiyu Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Mobile Robots ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimal Path ◽  
Particle Swarm ◽  
A Algorithm ◽  
Swarm Optimization ◽  
Elastic Particle

Path planning, as the core of navigation control for mobile robots, has become the focus of research in the field of mobile robots. Various path planning algorithms have been recently proposed. In this paper, in view of the advantages and disadvantages of different path planning algorithms, a heuristic elastic particle swarm algorithm is proposed. Using the path planned by the A* algorithm in a large-scale grid for global guidance, the elastic particle swarm optimization algorithm uses a shrinking operation to determine the globally optimal path formed by locally optimal nodes so that the particles can converge to it rapidly. Furthermore, in the iterative process, the diversity of the particles is ensured by a rebound operation. Computer simulation and real experimental results show that the proposed algorithm not only overcomes the shortcomings of the A* algorithm, which cannot yield the shortest path, but also avoids the problem of failure to converge to the globally optimal path, owing to a lack of heuristic information. Additionally, the proposed algorithm maintains the simplicity and high efficiency of both the algorithms.

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.

Task Allocation and Path Planning of a Multi-Robot System Using Heuristic Coupled Particle Swarm Optimization Algorithm

2020 ◽  
pp. 194-209
Author(s):  
Arindam Majumder ◽  
Rajib Ghosh
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimization Algorithm ◽  
Task Allocation ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
A Algorithm ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Multi Robot

This study deals with a plant layout where there were ninety predefined locations which have to be inspected by using three multiple robots in such a way that there would not be any collisions between the robots. A heuristic integrated multiobjective particle swarm optimization algorithm (HPSO) is developed for allocating tasks to each robot and planning of path while moving from one task location to another. For optimal path planning of each robot the research utilized A* algorithm. The task allocation for each robot is carried out using a modified multiobjective particle swarm optimization algorithm where the earliest completion time (ECT) inspired technique is used to make the algorithm applicable in multi robot task allocation problems. At the later stage of this study, in order to test the capability of HPSO an instance is solved by the algorithm and is compared with the existing solutions of a genetic algorithm with the A* algorithm. The computational results showed the superiority of the proposed algorithm over existing algorithms.

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