A gray-encoded, hybrid-accelerated, genetic algorithm for global optimizations in dynamical systems

摘要

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.