Optimization of truss-structures by genetic algorithms with narrowing space techniques
碩士 === 國立交通大學 === 土木工程系所 === 96 === Genetic algorithm (GA) is an efficient search technique in global space. It can not only deal with optimization problems with discrete variables, but also has ability to overcome the local minimization problems. Using GA to process the optimization design of discr...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2007
|
| Online Access: | http://ndltd.ncl.edu.tw/handle/09649887035355965475 |
| Summary: | 碩士 === 國立交通大學 === 土木工程系所 === 96 === Genetic algorithm (GA) is an efficient search technique in global space. It can not only deal with optimization problems with discrete variables, but also has ability to overcome the local minimization problems. Using GA to process the optimization design of discrete cross-section size for trusses has become a popular approach.For this problems the search space of GA may expand as the number of members of truss increasing; consquently it makes the process of global searching to converge slowly. This study proposes a narrowing search space technique for optimization design of trusses; it based on the strategy that reduces search space of GA to improve the speed of convergence. First it uses concept of fully stress design (FSD) to figure out optimal topology of a truss; then it takes a set of heuristic rules to enlarge cross-section sizes of a truss structure that was designed by FSD and takes the cross-section size that was enlarged to be the center of search space; finally it searches solutions of the problem nearby the center by GA. The narrowing space techniques that were implemented by this study were applied to solve constrainted optimization problems that include maximum and minimum cross-section size constraints, single load and multi-load case, and buckling stress constraints etc. Total eight planar and space truss design problems have been adapted to verify the performance of the porposed approach. The result reveales that the narrowing space techniques assist GA to reduce a lot of computational costs of optimization designs of truss structures.
|
|---|