Deterministic simulated annealing for system optimization

The authors propose a deterministic approach to simulated annealing for constrained optimization of continuous variables. The constrained region is dynamically divided into simplices, based on previous search points, using multidimensional Delaunay triangulations. For each simplex, the quotient of the exponential of the local-mean-function value and its hypervolume are calculated. The simplex with the minimum quantity described is selected to contain the next search point. This locates a region in the search space whose observed density of search points is the most inconsistent with the ideal density. The next search point is selected within this simplex such that the new simplices formed by the addition of the new search point each have an equal value of the quotient quantity described. The search is forced in a deterministic manner to generate the desired density of simulated annealing. The new method was applied to a standard set of test functions and the results are given