In this paper,the distributed Nash equilibrium searching problem of a game is investigated,where the feasible action sets are constrained by nonlinear inequalities and linear equations.Different from most of the existing investigations on distributed Nash equilibrium searching problems,we consider the case where both cost functions and feasible action sets depend on actions of all players,and each player can only have access to the information of its neighbors.To address this problem,a continuous-time distributed gradient-based projected algorithm is proposed,where a leader-following consensus algorithm is employed for each player to estimate actions of others.Under mild assumptions on cost functions and graphs,it is shown that players actions asymptotically converge to a generalized Nash equilibrium.Simulation examples are presented to demonstrate the effectiveness of the theoretical results.