Blind Source Separation Based on Multi-strategy Red-tailed Hawk Algorithm
The traditional optimization method for blind source separation is prone to getting trapped in local op-timal solutions,sensitive to parameter settings,and often lacks an effective adaptive adjustment mechanism for the dynamic characteristics of the problem.To address these issues,a blind source separation method based on multi-strategy red-tailed hawk algorithm(MSRTH)was proposed,which applied the Latin hypercube sampling to initialize the positions of red-tailed hawks in the original RTH algorithm.This approach enhanced the diversi-ty of the population and the global search capability of the algorithm,as well as improving its convergence speed.Furthermore,Gaussian mutation was introduced to provide the opportunity for the proposed algorithm to escape from the current local optimum solution.Simulation results showed that in comparation with the original RTH algo-rithm and genetic algorithm,this method had higher separation accuracy and faster convergence speed.
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