The Complex Blind Deflation Algorithm Based Particle Swarm Optimization with Survival of the Fittest Mechanism

For multi-constraint nonlinear optimization, this paper puts forward a complex blind deflation algorithm based particle swarm optimization with survival of the fittest mechanism (CBD-PSOSFM) which has faster convergence speed, and then gives a quantificational formula of the improved convergence speed, discusses implement method and the rule of parameters design; Because of the blind source separation (BSS) optimization characteristic in nature, the algorithm can be used to implement semi-BSS with nonlinear multi-constraint. For active object echo detection, the paper sets up fitness function with the multi-constraint like as kurtosis, energy and outline and forms the complex blind deflation algorithm. Finally, the simulation experiment of blind deflation to complex echo validates the algorithms validity and faster convergence capability.