| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 10415148 | Communications in Nonlinear Science and Numerical Simulation | 2005 | 9 Pages |
Abstract
A gray-encoded hybrid accelerating genetic algorithm (GHAGA) with Nelder-Mead simplex searching operator and simplex algorithm is developed for the global optimization of dynamical systems. The corresponding convergence theorem is developed to guarantee the new algorithm to be convergent. The efficiency of the new algorithm is verified by application of several well-investigated nonlinear functions. This algorithm overcomes any Hamming-cliff phenomena in existing genetic methods, and it is very efficient for optimizing nonlinear models compared to existing genetic algorithms and other traditional optimization methods.
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Authors
Xiaohua Yang, Zhifeng Yang, Gui-hua Lu, Jianqiang Li,