Genetic algorithm and flexible tolerance algorithm hybridized for global optimization problems with multiple constraints
Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 1267-1270 |
| Journal / Publication | Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University |
| Volume | 41 |
| Issue number | 11 |
| Publication status | Published - Nov 2007 |
| Externally published | Yes |
Link(s)
Abstract
A hybrid method combining a genetic algorithm with a flexible tolerance algorithm is proposed for global optimization problems with multiple nonlinear constraints and peaks. The adaptive genetic algorithm is used to localize the 'best' areas, while the flexible tolerance algorithm exploits this area by search mechanism for quasi-feasible point. To evaluate the efficiency of this method, a complex function with six peaks and four constraints is implemented and compared with the results supplied by sequential uniconstrained minimization technique (SUMT), which indicates that the hybrid method is able to improve convergence and reduce computing task greatly.
Research Area(s)
- Adaptive genetic algorithm, Flexible tolerance algorithm, Multiconstraint, Optimization
Citation Format(s)
Genetic algorithm and flexible tolerance algorithm hybridized for global optimization problems with multiple constraints. / Shang, Wanfeng; Zhao, Shengdun; Shen, Yajing et al.
In: Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University, Vol. 41, No. 11, 11.2007, p. 1267-1270.
In: Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University, Vol. 41, No. 11, 11.2007, p. 1267-1270.
Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal
