In this paper, a new hybrid simulated annealing algorithm for constrained global
optimization is proposed. We have developed a stochastic algorithm called ASAPSPSA that
uses Adaptive Simulated Annealing algorithm (ASA). ASA is a series of modifications to the
basic simulated annealing algorithm (SA) that gives the region containing the global
solution of an objective function. In addition, Simultaneous Perturbation Stochastic
Approximation (SPSA) method, for solving unconstrained optimization problems, is used to
refine the solution. We also propose Penalty SPSA (PSPSA) for solving constrained
optimization problems. The constraints are handled using exterior point penalty functions.
The combination of both techniques ASA and PSPSA provides a powerful hybrid optimization
method. The proposed method has a good balance between exploration and exploitation with
very fast computation speed, its performance as a viable large scale optimization method
is demonstrated by testing it on a number of benchmark functions with 2 - 500 dimensions.
In addition, applicability of the algorithm on structural design was tested and successful
results were obtained