Automatic selection of sub-populations and minimal spanning distances for improved numerical optimization
J.A. Rumpler, Frank W. Moore · 2002
This paper presents a modified differential evolution algorithm that is capable of automatically discovering an arbitrarily large number of global optima in an arbitrarily complex solution space. Previous research is extended in two ways: first, the algorithm automatically determines the number of sub-populations that are necessary to maximize the number of optimal solutions found. Second, the algorithm automatically determines the appropriate minimal spanning distance between elements from each sub-population. These extensions greatly increase the overall power and efficiency of the DE algorithm for the numerical optimization of multidimensional objective functions. Results for several benchmark problems are described.