Blind source separation algorithm based on modified bacterial colony chemotaxis

Most blind source separation (BSS) algorithm use single-point optimization method which always have the disadvantage of slow convergence speed, bad separate precision and easily getting into the local optimization. In view of these disadvantages, recently, Chen proposed a multiple-point optimization algorithm for BSS named DPBCC, which overcome these disadvantages at a certain extent. But DPBCC uses the superior bacterial random perturbation strategy to solve the problem of local convergence, which cannot ensure that after random perturbation it will be an ergodic search of the domain. So the ability of global convergence still has to improve. This paper proposes a modified bacterial colony chemotaxis algorithm (CBCC) for BSS, combined the strategy of chaos search with the strategy of neighborhood random search, reaching to an ergodic search of the entire domain, solving the local convergence better, improving the convergence speed and separate precision further. Take the BSS algorithm based on kurtosis under the instantaneous linear model for example to do computer simulation. The results validate the superiority of CBCC by comparing with the existing ones.