Abstract: Simulated Annealing (SA) is a powerful stochastic search method applicable to a wide range of problems. It can produce very high quality solutions for hard combinatorial optimization problems. SA can be generalized to fit non-convex cost-functions arising in a variety of problems which is known as Boltzmann Annealing (BA). The purpose of describing Simulated Quenching (SQ) and Fast Annealing (FA) is to highlight the importance of Adaptive Simulated Anealing(ASA). ASA is a global optimization algorithm based on an associated proof that the parameter space can be sampled much more efficiently than by using the previous Simulated algorithm.