Optical　aberrations　introduced　by　sample　or　system　elements　usually　degrade　the　image　quality　of　a　microscopic　imaging　system.　Computational　adaptive　optics　has　unique　advantages　for　3D　biological　imaging　since　neither　bulky　wavefront　sensors　nor　complicated　indirect　wavefront　sensing　procedures　are　required.　In　this　paper,　a　stochastic　parallel　gradient　descent　computational　adaptive　optics　method　is　proposed　for　high-efficiency　aberration　correction　in　the　fluorescent　incoherent　digital　holographic　microscope.　The　proposed　algorithm　possesses　the　advantage　of　parallelly　estimating　various　aberrations　with　fast　convergence　during　the　iteration;　thus,　the　wavefront　aberration　is　corrected　quickly,　and　the　original　object　image　is　retrieved　accurately.　Owing　to　its　high-efficiency　adaptive　optimization,　the　proposed　method　exhibits　better　performances　for　a　3D　sample　with　complex　and　anisotropic　optical　aberration.　The　proposed　method　can　be　a　powerful　tool　for　the　visualization　of　dynamic　events　that　happen　inside　cells　or　thick　tissues.