Nonlinear blind separation algorithm based on multiobjective evolutionary algorithm
Hai‐Lin Liu, Xie Sheng-li · Systems engineering and electronics · 2005
In nonlinear blind source separation, the approach for invertible functions is very difficulty due to the existence of many local minima. For separating source signals efficiently, a nonlinear blind separation algorithm based on specific-designed multiobjective evolutionary algorithm is proposed. As defining a novel kind of multiple fitness functions by max-min strategy, the evolutionary algorithm can explore the search space uniformly, keep the diversity of the population, and escape from local optima. The simulation results demonstrate that the proposed algorithm is efficient.