Application research of fuzzy optimization based on genetic algorithm

Author(s):  
Panxiang Yue
2009 ◽  
Vol 419-420 ◽  
pp. 185-188 ◽  
Author(s):  
Song Lin Yang ◽  
Zhao Long Yang ◽  
Lian Xiang Ma ◽  
Hong Qin Zhang

The authors built up a new algorithm called FUZZY-D-P-GA which was based on the fuzzy optimization, the genetic algorithm, delicate variables’ segments thinking & parallel principle. Meanwhile, they also succeed in applying this composite method on the synthetically optimization of navigation performance and structure characteristic of ships. Through a large number of calculation, the results showed that this kind of algorithm had high efficiency and it was also reliable.


2014 ◽  
Vol 519-520 ◽  
pp. 1468-1471
Author(s):  
Jun Quan Gong ◽  
Xiao Hong Qin

Enterprise often face to limit financial resources but also have to consider how to invest effectively on a number of projects in the various factors of the risks and benefits in different periods. In order to assure the optimal investment results of capital investment, this paper has established dynamic programming model which is multi-dimensional and multi-objective and fuzzy optimization, dynamic programming and genetic algorithm is combination to solve investment decision of enterprise. At last, this paper through an example to verify the validity of dynamic programming model.


Author(s):  
FA-CHAO LI ◽  
CHEN-XIA JIN ◽  
PAN-XIANG YUE

By using the restricted and complementary relationship of the principle and secondary indexes, providing the description of the compound quantification of the fuzzy number, and analyzing the essential characteristic of fuzzy decision, we propose a kind of fuzzy genetic algorithm based on the principle index (PO-FGA for short) to deal with the fuzzy optimization and programming problems with fuzzy coefficients, fuzzy variables and fuzzy constraints. The concrete solution method is presented in accordance with the strategy of the unconditional penality transformation with conditional constrains. Then consider its convergence by using Markov chain theory and analyze its performance through two examples. All these indicate that this kind of algorithm is of faster speed of convergence, smaller number of iterations, has lower chances of trapping into the state of premature convergence and can be widely used in many problems of optimization.


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