In this paper scatter search algorithm has been hybridized with simulated annealing algorithm. Scatter search algorithm has some disadvantages which will be solved by using concepts of simulated annealing algorithm. Simulated annealing algorithm will provide scatter search algorithm with more of exploration for problem search space, this will increase ratio of getting optimal solutions. The hyb...

Artificial intelligence methods and search techniques such as simulated annealing and simultaneous perturbation can be used in simulation optimization of complex stochastic systems. Simultaneous perturbation has also been applied in the area of neural networks. Simulation optimization methods are often used when it is difficult to obtain the closed form of the objective function, but it is poss...

The Metropolis algorithm is simulated annealing with a fixed temperature. Surprisingly enough, many problems cannot be solved more efficiently by simulated annealing than by the Metropolis algorithm with the best temperature. The problem of finding a natural example (artificial examples are known) where simulated annealing outperforms the Metropolis algorithm for all temperatures has been discu...

Adaptive simulated annealing (ASA) is a global optimization algorithm based on an associated proof that the parameter space can be sampled much more efficiently than by using other previous simulated annealing algorithms. The author’s ASA code has been publicly available for over two years. During this time the author has volunteered to help people via e-mail, and the feedback obtained has been...

Stochastic global optimization is a very important subject, that has applications in virtually all areas of science and technology. Therefore there is nothing more opportune than writing a book about a successful and mature algorithm that turned out to be a good tool in solving difficult problems. Here we present some techniques for solving several problems by means of Fuzzy Adaptive Simulated ...