Chaos and Gaussian local optimization based hybrid differential evolution(CGHDE) is proposed to solve the premature convergence and low precision of standard differnential evolution(DE) when applied to high-dimensional complex engineering problems. By means of the randomicity of chaotic local search, the CGHDE algorithm tends to explore in a wide search space to overcome the premature in the earlier evolution phase, and then performs exploitation to refine the optimum by using Gaussian optimization to improve the output in the later run phase. Simulations show that, CGHDE algorithm is
not as sensitive to function dimensions as standard DE and has the advantages of powerful optimizing ability, more stability, higher optimizing precision and suitable for high-dimensional complex functions optimization.