Job shop scheduling by simulated annealing
Operations Research
The annealing evolution algorithm as function optimizer
Parallel Computing
Machining condition optimization by genetic algorithms and simulated annealing
Computers and Operations Research
An efficient simulated annealing algorithm for design optimization of truss structures
Computers and Structures
Size optimization of space trusses using Big Bang-Big Crunch algorithm
Computers and Structures
Parameter estimation approach in groundwater hydrology using hybrid ant colony system
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
| Hi-index | 0.00 |
Although simulated annealing (SA) is one of the easiest optimization algorithms available, the huge number of function evaluations deters its use in structural optimizations. In order to apply SA in structural optimization efficiently the number of finite element analyses (function evaluations) has to be reduced as much as possible. Two methods are proposed in this paper. One is to estimate the feasible region using linearized constraints and the SA searches proceed in the estimated feasible region. The other one makes SA search start with an area containing higher design variable values. The search area is then gradually moved toward the optimum point in the following temperatures. Using these approaches, it is hopeful that the number of finite element analyses in the infeasible region can be greatly reduced. The efficiency of SA is thus increased. Three examples show positive results by these methods.