Optimal design of system reliability by an improved genetic algorithm

1996 ◽  
Vol 79 (2) ◽  
pp. 41-51 ◽  
Author(s):  
Takao Yokota ◽  
Mitsuo Gen ◽  
Kenichi Ida ◽  
Takeaki Taguchi
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
System Reliability ◽  
Improved Genetic Algorithm

Layout Design of Naval Gun Magazine Based on Improved GA

2009 ◽  
Vol 419-420 ◽  
pp. 669-672
Author(s):  
Wei Fu ◽  
Sheng Hai Hu ◽  
Yang Ge
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
System Reliability ◽  
Layout Design ◽  
Mutation Operator ◽  
Improved Genetic Algorithm ◽  
Encoding Scheme ◽  
Performance Constraints ◽  
Naval Gun ◽  
Polygon Method

In this paper a magazine layout optimization model with performance constraints is described and the objective function and its constraints of magazine layout are established. A multiobjective optimizations layout model based on polygon method is put forward. The model deals with a series of constraints such as geometry constraints, in-and-out point position optimization, magazine capacity and system reliability and safety. An improved genetic algorithm (GA) is proposed in this paper based on the new encoding scheme and logical mutation operator. The algorithm solved the problem of multiobjective layout design with performance constraints.

Maximum Likelihood Parameter Estimation of CNC System Reliability

2014 ◽  
Vol 487 ◽  
pp. 282-285
Author(s):  
Yan Gu ◽  
Yi Qiang Wang ◽  
Xiao Qin Zhou ◽  
Xiu Hua Yuan
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Maximum Likelihood ◽  
System Reliability ◽  
Simulated Annealing Algorithm ◽  
Likelihood Function ◽  
Estimation Method ◽  
Distribution Model ◽  
Improved Genetic Algorithm ◽  
Cnc System

In order to increase calculation accuracy of CNC system reliability, this paper proposed a maximum likelihood parameter estimation method based on improved genetic algorithm. In the parameter estimation process for CNC system reliability distribution model, the maximum likelihood function value was gained by improving genetic algorithm through simulated annealing algorithm. Parameter estimation was carried out by setting Weibull distribution as an example. The result shows that the improved genetic algorithm can increase solution efficiency and convergence rate. Besides, it can effectively estimate parameters of reliability distribution model.

A Novel Improved Genetic Algorithm and Application in Mechanical Optimal Design

2009 ◽  
Vol 628-629 ◽  
pp. 263-268 ◽  
Author(s):  
Zhong Min Wang ◽  
Yu Jun Cai ◽  
D.H. Miao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimal Design ◽  
Hybrid Genetic Algorithm ◽  
Pso Algorithm ◽  
Improved Genetic Algorithm ◽  
Neural Network Method ◽  
Random Direction ◽  
Network Method ◽  
Species Population

A new improved genetic algorithm (IGA) based on floating point encoding is proposed. Firstly, IGA uses information entropy to produce better initialized species population. Secondly, after synthetically studying the searching properties of crossover operator in GA, it designs a new crossover strategy that effectively increases searching efficiencies of IGA. Thirdly, to avoid searching being trapped in local minimum, it designs a chaos degenerate mutation operator that makes the searching fast converge to a global minimum. At last IGA is used to solve the problem of the optimal design to crane girder, which is a typical problem of mechanical optimal design. Compared with the traditional random direction method, neural network method, genetic-neural network method, hybrid genetic algorithm, chaos-GA, PSO algorithm, chaos-PSO algorithm and standard GA, IGA shows better performance at the aspect of solution precision and convergence speed than that of these algorithms.

Investigation of Roll Pass Optimal Design Based on IGA

2011 ◽  
Vol 211-212 ◽  
pp. 195-199 ◽  
Author(s):  
Bin Huang ◽  
Ke Xing ◽  
Kazem Abhary ◽  
Sead Spuzic
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Roll Pass Design ◽  
Improved Genetic Algorithm ◽  
Pass Design ◽  
Roll Pass ◽  
Empirical Formulas ◽  
Design And Optimization ◽  
Rolling System ◽  
Rod Rolling

The primary purpose of this study is to develop a genetic algorithm based computer-aided roll pass optimal design (CAROD) system to support the generalized roll pass design for rod rolling, where the final products are round bars with different sizes. The system was developed to minimize the number of roll passes, decrease the trails and errors in industry, as well as extend the work range of multi-pass rolling systems for rod rolling. Parametric equations were established for geometrical modeling and graphic plotting, which can realize to the parametric transformation for roll pass design and optimization. A methodology based on a hybrid model was proposed to choose passes with different profiles for the multi-pass rod rolling system. In addition, an improved genetic algorithm (IGA) was employed for the optimization of roll passes. A MATLAB program was designed to achieve all these objectives. To reduce the complexity and computational burden of the software, some reliable empirical formulas were applied in this system. Finally, the proposed approach has been applied in a rod rolling system; through simulation and comparison of results against analytical solutions, numerical analysis and experimental data presented by other researchers, it was found that this system is reliable, effective and easier to use.

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