In this paper, a simulated-annealing-based method called Filter
Simulated Annealing (FSA) method is proposed to deal with the
constrained global optimization problem. The considered problem is
reformulated so as to take the form of optimizing two functions;
the objective function and the constraint violation function.
Then, the FSA method is applied to solve the reformulated problem.
The FSA method invokes a multi-start diversification scheme in
order to achieve an efficient exploration process. To deal with
the considered problem, a filter-set-based procedure is built in
the FSA structure. Finally, an intensification scheme is applied
as a final stage of the proposed method in order to overcome the
slow convergence of SA-based methods. The computational results
obtained by the FSA method are promising and show a superior performance of the proposed method,
which is a point-to-point
method, against population-based methods.