Skip to main content
U.S. Department of Energy
Office of Scientific and Technical Information

Dynamic multimodal function optimization using genetic algorithms

Conference ·
OSTI ID:10170203
 [1];  [2]
  1. Lawrence Livermore National Lab., CA (United States)
  2. California Univ., Davis, CA (United States). Dept. of Applied Science
Many optimization techniques work well for unimodal functions. If applied to multimodal functions, they tend to converge to only one of the many peaks. Optimization of multimodal functions becomes even more difficult if the function parameters change dynamically. Genetic algorithms have been successfully applied by several investigators for static optimization of multimodal functions. This modest success is primarily due to the ability of genetic algorithms to locate more than one peak. In this paper we introduce a combination of selection and replacement operators that is suitable for multimodal function optimization in a dynamic environment using various test functions, performance of this new operator is studied. Utility of this new operator to multimodal function optimization in a dynamic environment is described.
Research Organization:
Lawrence Livermore National Lab., CA (United States)
Sponsoring Organization:
USDOE, Washington, DC (United States)
DOE Contract Number:
W-7405-ENG-48
OSTI ID:
10170203
Report Number(s):
UCRL-JC--111089; CONF-9208118--1; ON: DE92019602
Country of Publication:
United States
Language:
English

Similar Records

Dynamic multimodal function optimization using genetic algorithms
Conference · Mon Jul 20 00:00:00 EDT 1992 · OSTI ID:7104669

Niching genetic algorithm with restricted competition selection for multimodal function optimization
Conference · Sat May 01 00:00:00 EDT 1999 · IEEE Transactions on Magnetics · OSTI ID:6207340

Reactive power optimization by genetic algorithm
Journal Article · Sun May 01 00:00:00 EDT 1994 · IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (United States) · OSTI ID:7251013