Improved FGA and Its Applications in the Optimal Design of Four Bar Mechanism
The genetic algorithm can effectively handle some complex optimal problems that the conventional optimization methods can’t solve. However,the traditional genetic algorithm has many defects,such as falling easily into local solution,slower convergence speed and the poor effect of optimal problems wi...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2017-01-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.07.037 |
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| Summary: | The genetic algorithm can effectively handle some complex optimal problems that the conventional optimization methods can’t solve. However,the traditional genetic algorithm has many defects,such as falling easily into local solution,slower convergence speed and the poor effect of optimal problems with constraints. An improved float-encoding genetic algorithm（ FGA） solving optimal problems with inequality constraints is proposed. This method has the advantages of high convergence efficiency,good stability and strong local search capability. It is used to the optimal design of crank-link mechanism,and the optimal results show that the improved genetic algorithm has a better effect than the traditional genetic algorithm. |
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| ISSN: | 1004-2539 |