Title :
Automatic selection of sub-populations and minimal spanning distances for improved numerical optimization
Author :
Rumpler, James A. ; Moore, Frank W.
Author_Institution :
Dept. of Comput. Sci. & Syst. Analysis, Miami Univ., Oxford, OH, USA
Abstract :
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
Keywords :
evolutionary computation; numerical analysis; complex solution space; global optima; minimal spanning distance; modified differential evolution algorithm; multidimensional objective functions; numerical optimization; sub-population selection; Algorithm design and analysis; Computer science; Control system synthesis; Costs; Design optimization; Digital filters; Fuzzy logic; Harmonic filters; Multidimensional systems; Simulated annealing;
Conference_Titel :
Evolutionary Computation, 2001. Proceedings of the 2001 Congress on
Conference_Location :
Seoul
Print_ISBN :
0-7803-6657-3
DOI :
10.1109/CEC.2001.934368
