Improved fruit fly algorithm on structural optimization
Abstract To improve the efficiency of the structural optimization design in truss calculation, an improved fruit fly optimization algorithm was proposed for truss structure optimization. The fruit fly optimization algorithm was a novel swarm intelligence algorithm. In the standard fruit fly optimiza...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2020-02-01
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| Series: | Brain Informatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40708-020-0102-9 |
| Summary: | Abstract To improve the efficiency of the structural optimization design in truss calculation, an improved fruit fly optimization algorithm was proposed for truss structure optimization. The fruit fly optimization algorithm was a novel swarm intelligence algorithm. In the standard fruit fly optimization algorithm, it is difficult to solve the high-dimensional nonlinear optimization problem and easy to fall into the local optimum. To overcome the shortcomings of the basic fruit fly optimization algorithm, the immune algorithm self–non-self antigen recognition mechanism and the immune system learn–memory–forgetting knowledge processing mechanism were employed. The improved algorithm was introduced to the structural optimization. Optimization results and comparison with other algorithms show that the stability of improved fruit fly optimization algorithm is apparently improved and the efficiency is obviously remarkable. This study provides a more effective solution to structural optimization problems. |
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| ISSN: | 2198-4018 2198-4026 |