<p>In this paper, a novel adaptive combined conjugate gradient (CCG) method is proposed to solve multiobjective optimization problems (MOPs), in which the combined coefficients of all gradients update adaptively. The search direction of the CCG method is determined only by the gradient information of the involved functions and the conjugate term, which is proved to satisfy descent condition and sufficient descent condition. The global convergence of the CCG method with the Wolfe-like line search is established under some suitable assumptions. We also achieve that the iterative sequence generated by the CCG method converges weakly to some Pareto critical point without the convexity assumption. As special cases, the global convergence of the CCG method with special conjugate parameters such as FR, CD, DY and modified DY parameters are also derived under mild conditions. Numerical experiments demonstrate the effectiveness and superiority of the CCG method, particularly in its ability to generate Pareto frontiers. In addition, the proposed method is validated for the high-dimensional MOPs by the numerical results.</p>