A Novel Blind Source Separation Approach Based on Invasive Weed Optimization

Traditional optimization algorithms for Blind Source Separation (BSS) are mainly based on the gradient, which requires the objective function to be continuous and differentiable, and have some defects such as slow convergence speed or poor accuracy of the solution. To solve these problems, a novel BSS approach based on Invasive Weed Optimization (IWO) is proposed in this paper. By maximizing a negentropy-based objective function, simulation experiments confirm the effectiveness of the proposed algorithm.