Independent component analysis (ICA) is a typical blind source separation algorithm.Nowadays,it has been applied to s o lve the problem of hyperspectral unmixing.Under linear mixture model,a pixel spe ctrum of hyperspectral image can be approximated to a collection of constituent spectra, called endmember and a corresponding set of fractional abundances,one set per pixel.However,the ac tual ground-object distribution demands that the abundance should satisfy both abundance nonnegativity constraint (ANC) and abundance sum-to-one constraint (ASC).Thus,the independence requirement of ICA conflicts with these t wo constraints and it′s easy to converge to local minima with gradient algorithm to optimize the relevant obj ective function.To solve this problem,we propose to combine the non-negative independent component analysis model with abundance sum-to-one constraint to construct a novel objective function,and introduce c uckoo search (CS) algorithm to optimize the function with its excellent global searching ability.Experimental results o n synthetic data and real hyperspectral data indicate that the proposed algorithm can effectively solve the above problems and obtain more accurate results without any spectral prior knowledge.When signal- to-noise ratio (SNR) is set to 30dB and purity of pixel is set to 0.8,these unmixing indexes,which are spectra l angel distance (SAD) and mean square error (RMSE),can reach below 0.03.