Uncertain environments and inefficient decision analysis restrict the efficient utilization of depletable public resources by multiagents, especially for the scenario involved with multiobjective game dilemmas and weak scalability of decision making. To address the above conundrums, this article proposes a multilayer games framework that integrates cognition, decision making, and countermeasures (CDCs). Through the transformation of agent preference to alliance communication structure, a cooperation-competition topology network (CCTN) model is constructed, which improves the convergence and solution efficiency of the game model. In view of the Gaussian kernel ascending dimension mapping, a game equilibrium particle swarm optimization (GEPSO) algorithm is designed to improve the efficiency of finding equilibrium solutions and solve the nondeterministic polynomial (NP) problem of multiobjective games. To validate the effectiveness and performance of the proposed methodology, a case study of collaborative detection of multivehicle is conducted using the proposed framework and model.