Control and synchronization of chaotic systems by an improved biogeography-based optimization algorithm

Biogeography-based optimization algorithm (BBO) is a relatively new optimization technique which has been shown to be competitive to other biology-based algorithms. However, there is still an insufficiency in BBO regarding its migration operator, which is good at exploitation but poor at exploration. To address this concerning issue, we propose an improved BBO (IBBO) by using a modified search strategy to generate a new mutation operator so that the exploration and exploitation can be well balanced and then satisfactory optimization performances can be achieved. In addition, to enhance the global convergence, both opposition-based learning methods and chaotic maps are employed, when producing the initial population. In this paper, the proposed algorithm is applied to control and synchronization of discrete chaotic systems which can be formulated as high-dimension numerical optimization problems with multiple local optima. Numerical simulations and comparisons with some typical existing algorithms demonstrate the effectiveness and efficiency of the proposed approach.