Abstract: According to the characteristics of the constrained multiobjective optimization problem, the unconstrained multiobjective aritificial physics optimization algorithm(MOAPO) is introduced into the field of constrained multiobjective optimization on the basis of aritificial physics optimization algorithm(APO). The judgment criterion of constraint violation degree is put forward, and a more effective constraint processing technology is taken. Then a swarm intelligence improved constrained rank multiobjective aritificial physics optimization (ICRMOAPO) algorithm based on sequence value and crowding degree applied to solve the problem of constrained multiobjective optimization is constructed. In random searching, the factor of gravity and inertia weight are adjusted dynamically, to enhance the diversity of the noninferior solution set. The comparative experiments between constrained rank multiobjective aritificial physics optimization(CRMOAPO), nondominated sorting genetic algorithm(NSGA), and multiobjective genetic algorithm(MOGA) show that the effectiveness of the proposed algorithm has better performance of distribution and convergence, thereby providing a new train of thought and a method for solving the constrained multiobjective optimization problem.