Shape and Cross-Section Optimization of Spatial Grid Structures Using Genetic Algorithm

2012 ◽  
Vol 479-481 ◽  
pp. 1463-1467
Author(s):  
Jian Chun Xiao ◽  
Jing Chen ◽  
Qi Li ◽  
Shao Quan Xia
Keyword(s):  
Genetic Algorithm ◽  
Cross Section ◽  
Physical Property ◽  
Structural Deformation ◽  
Improved Genetic Algorithm ◽  
Integer Variables ◽  
Optimization Function ◽  
Spatial Grid ◽  
The Stability ◽  
Mixed Codes

The optimization of the structures is difficult because the variables have different physical property or different quantitative attribute. The shape and cross-section optimization of spatial grid structures is performed by an improved genetic algorithm. The constraint conditions are composed of the structural deformation, the stability of the compressive members, the slender ratios, and etc. The treatment of the constraint conditions and the optimization function gives an unconstrained analytic function by adopting Lagrange multipliers. The method enhances the running efficiency of the genetic algorithm. The programme for structural optimization containing the mixed codes of continuous real variables, discontinuous real variables, and integer variables is coded by using MATLAB Toolbox functions for genetic algorithm. The analysis of examples shows that the programme is reliable, and the convergence of the algorithm is fast as well.

The Study of Optimal Fuzzy PID-Smith Control Based on Genetic Algorithm

2013 ◽  
Vol 313-314 ◽  
pp. 448-452
Author(s):  
Dian Ting Liu ◽  
Hai Xia Li
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Control Method ◽  
Order System ◽  
Membership Functions ◽  
Fuzzy Pid ◽  
Improved Genetic Algorithm ◽  
Simulation Results ◽  
The Stability ◽  
Better Than

In this paper, the improved genetic algorithm is applied to optimize the quantization factors and the scaling factors of fuzzy control, and the optimized rule table and membership functions is obtained according to certain performances. Then a kind of optimal fuzzy PID-Smith control method based on genetic algorithm is proposed and its simulation model is built in this paper, a second-order system is simulated and analyzed. The results show that requirements of deterministic performances of the new control method are better than the conventional methods through the simulation results in the stability, rapidity and robustness.

scholarly journals A Genetic-Algorithm-Based Optimization Routing for FANETs

2021 ◽  
Vol 15 ◽  
Author(s):  
Xing Wei ◽  
Hua Yang ◽  
Wentao Huang
Keyword(s):  
Genetic Algorithm ◽  
Routing Protocol ◽  
High Mobility ◽  
Destination Node ◽  
Improved Genetic Algorithm ◽  
On Demand ◽  
Link Stability ◽  
Variation Operators ◽  
The Stability ◽  
Route Search

In view of the characteristics of high mobility of FANETs nodes, combined with the features of Topology-based class routing protocol on-demand search, a Genetic-algorithm-based routing (GAR) protocol is proposed for FANETs which based on improved genetic algorithm for FANETs route search, and it taking into account the link stability, link bandwidth, node energy, and other factors. GAR improves the selection, crossover, and variation operators of the genetic algorithm so that GAR can finally plan an optimized route from the communication initiating node to the destination node quickly using a smaller cost. The experimental results show that GAR can largely improve the throughput, reduce the delay and improve the stability of the network, which is more suitable for FANETs.

AN IMPROVED GENETIC ALGORITHM AND ITS APPLICATION TO CRACK EXAMINATION

2014 ◽  
Vol 06 (01) ◽  
pp. 1450010 ◽  
Author(s):  
RUIPENG GAO ◽  
CHUNYANG SHANG ◽  
JIAN ZHUANG ◽  
HANG JIANG
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Research Work ◽  
Current Method ◽  
Field Testing ◽  
Improved Genetic Algorithm ◽  
Examination Method ◽  
Optimization Function ◽  
Convergence Performance

In this paper, a rail crack examination method based on an optimized complex evolutionary algorithm is proposed. The proposed approach has overcome the problems in which the fault diagnosis of wavelet packet decomposition is easily influenced by environmental factors, and the traditional genetic algorithm has a slow convergence speed and is prone to prematurity. In current method, to control the convergence performance, the diversity probability Pg is added in the update operation. According to the characteristics of rail crack fault signal, optimization function and cost function which consider the information in time and frequency domains are constructed and the information extraction is effectively realized. From field testing, the obtained results demonstrate that the present crack fault examination algorithm has higher accuracy rate, faster convergence rate and good stability. The present research work not only make up for the deficiency of traditional algorithm, but also provides a new examination method for wheel and rail noise fault diagnosis.

Study on the Stability of Virtual Network Mapping Algorithm Based on the Improved Genetic Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 3696-3700
Author(s):  
Jun Wei Ge ◽  
Yun Yu ◽  
Yi Qiu Fang
Keyword(s):  
Genetic Algorithm ◽  
Virtual Network ◽  
Improved Genetic Algorithm ◽  
Mapping Algorithm ◽  
Control Threshold ◽  
Physical Node ◽  
Virtual Network Mapping ◽  
The Stability ◽  
Network Mapping ◽  
Virtual Mapping

A based on the improved genetic algorithm of the stability is presented, for the current virtual network mapping study based on the underlying resources load imbalance. The algorithm consider for the constraint of the underlying physical node, link resources and the parameters of virtual network requests. Join control threshold α to decide to accept the request. Use the improved genetic algorithm to automatically adapt to the current load overheating network node, choose the best physical link and line up a virtual mapping. As can be seen through the analysis of simulation results, the algorithm can process the request maps faster than others algorithm, improve the stability and the load balancing capability.

Fractionation of polymethylene chains: An electron crystallographic investigation

1986 ◽  
Vol 44 ◽  
pp. 794-795
Author(s):  
Douglas L. Dorset
Keyword(s):  
Cross Section ◽  
Binary Systems ◽  
Linear Chain ◽  
Pure Component ◽  
Organic Substrates ◽  
Crystallographic Investigation ◽  
X Ray Diffraction ◽  
X Ray ◽  
Molecular Sizes ◽  
The Stability

A variety of linear chain materials exist as polydisperse systems which are difficultly purified. The stability of continuous binary solid solutions assume that the Gibbs free energy of the solution is lower than that of either crystal component, a condition which includes such factors as relative molecular sizes and shapes and perhaps the symmetry of the pure component crystal structures.Although extensive studies of n-alkane miscibility have been carried out via powder X-ray diffraction of bulk samples we have begun to examine binary systems as single crystals, taking advantage of the well-known enhanced scattering cross section of matter for electrons and also the favorable projection of a paraffin crystal structure posited by epitaxial crystallization of such samples on organic substrates such as benzoic acid.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

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