Abstract
The high efficiency of the Monte Carlo optimization algorithm developed by Pulfer and Waine14 is due to the discovery of a novel sampler that combines randomized guided step sizes with a random direction search strategy. We modified this algorithm to use a preset number of optimally sequenced steps to bound the randomly chosen step length. This has the effect of both spanning the response surface rapidly and escaping local optima efficiently. Coupled to changes in both sampling strategy and termination criteria, the resulting guided Monte Carlo (GMC) numerical search algorithm is shown to solve the global optimization problem effectively. Fifteen multidimensional benchmark test functions having differing characteristics such as numerous local optima or very sharp optima, very shallow optima, variables with differing influence over the function, and high dimensionality, were used to test the efficacy of the GMC algorithm. It was successful in solving them all, with a majority converging 100% of the time ou...
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