An improved dynamical evolutionary algorithm based on chaotic

An improved dynamical evolutionary algorithm based on the chaotic is proposed for optimizing. The new algorithm makes full use of initial value sensitivity and track ergodicity of chaos, overcoming the disadvantage of big searching dead zone existed in conventional chaotic mutation model. To achieve high performance in optimizing, the chaotic search mechanism is embedded in the standard dynamical evolutionary algorithm adaptively to avoid the stagnancy of population and increase the speed of convergence. The method keeps balance between the global search and the local search. It has been compared with other methods. In comparison, the proposed method shows its superiority in convergence property and robustness. It is validated by the simulation results.