Optimal design of DFG-based wavelength conversion based on hybrid genetic algorithm

2005 ◽  
Author(s):  
Xueming Liu ◽  
Yanhe Li
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Wavelength Conversion ◽  
Hybrid Genetic Algorithm

Improved Genetic Algorithms Based Optimal Design of The Shuangji River Aqueduct

2011 ◽  
Vol 201-203 ◽  
pp. 1288-1291
Author(s):  
Xin Li Bai ◽  
Wei Yu ◽  
Dan Fei Wang ◽  
Yuan Yuan Fan
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Structural Optimization ◽  
Large Scale ◽  
Hybrid Genetic Algorithm ◽  
Computer Software ◽  
Search Method ◽  
Discrete Variables ◽  
Original Design ◽  
Design Plan

The simple genetic algorithm (SGA) is taken as the global search method, and the traditional direct search method for mixed-discrete variables as the local search method. The improved (hybrid) genetic algorithm (IGA) is obtained by improving the SGA. And through the introduction of penalty constraints, the problem dealing with the constraints in GA is successfully resolved. A mathematical model for structural optimization of aqueduct is established, and computer software is developed for structural optimization of large-scale aqueduct based on IGA. Using this program, the Shuangji River aqueduct is optimized and Rectangle-sectioned aqueduct design plan is obtained. Compared with the original design plan, optimal design is very economical and was adopted by Design Institute.

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.

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