  Quasi-isentropic compression is an effective method to achieve high-density
and high-temperature implosion in laser-driven inertial confinement fusion
(ICF). However, it requires precise matching between the laser profile and the
target structure. Designing the optimal laser profile and the corresponding
target for ICF is a challenge due to the large number of parameters involved.
In this paper, we present a novel method that combines random walk and Bayesian
optimization. The basic sampling data for Bayesian optimization are a series of
laser pulse profiles and target structures that can produce relatively high
areal densities obtained by the random walk method. This approach reduces the
number of samples required for Bayesian optimization and mitigates low
efficiency in the latter stages of the random walk method. The method also
reduces the randomness in the optimization process and enhances the
optimization efficiency. It should have important applications in ICF research.
