Comparison of global search methods for design optimization using simulation

A methodology for the application of global search methods for optimizing the results of a computer simulation is presented. Specific global optimization methods including simulated annealing, genetic algorithms, and Bayesian techniques are discussed in terms of their strengths and weaknesses as applied to this methodology. In particular, the effects of simulation time, constraints, dimensionality, and computational complexity are considered as they relate to the choice of algorithms. Simulated annealing and genetic algorithms perform similarly, yet differ in many ways from the class of Bayesian algorithms. Bayesian algorithms spend additional computation time in modeling all past values of the unknown function in an effort to minimize the number of evaluations of the function. These methods would be the algorithms of choice for determining the optimal design via simulation, provided the number of design variables is less than 10 and the time required to run a single simulation is large compared with the time it takes the algorithm to determine the next point