A HYBRID CHAOS OPTIMAZATION ALGORITHM FOR GLOBAL OPTIMIZATION OF NONLINEAR FUNCTIONS

The chaos optimization technique proposed in recent years searches the global optimum, using the properties of ergodicity and randomness of the chaos sequence. The chaos optimization algorithms in the literature are all based on Logistic map. However, it is noticed that the probability density function of chaotic sequence of Logistic map is Chebyshev-type function, which may affect the global searching capacity and computational efficiency of chaos optimization algorithm severely. In this paper, considering the property of probability density function of chaotic sequence, an improved hybrid chaos-BFGS optimization algorithm is established by eliminating the bad design points during the chaos searching process. Numerical results of the improved chaos-BFGS algorithm for the nonlinear test functions show that the probability of getting the global optimum increases by 10-30 % for the same number of chaos search, and the number of chaos search reduces 8-10 times with the probability of 1.0 for obtaining the global optimum, compared to the results in the reference. In addition, the fine search strategy is introduced into the hybrid algorithm so that it can optimize the nonlinear functions with large boundary constraints.