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

Volume

1

fYear

2001

fDate

2001

Firstpage

38

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2001. Proceedings of the 2001 Congress on

Conference_Location

Seoul

Print_ISBN

0-7803-6657-3

Type

conf

DOI

10.1109/CEC.2001.934368

Filename

934368