Considering that the multi-objective particle swarm optimization(MOPSO) algorithm can not give
simultaneously attention to convergence performance and solutions quality when it deals with constrained optimization
problems, an improved MOPSO algorithm based on immune network(IN-MOPSO) is proposed. In IN-MOPSO, the
information of populations exchange through immune network in IN-MOPSO in order to achieve cooperative search of
both MOPSO and artificial immune network(AIN) for solution space. Meanwhile, an improved migration method of particle
velocity, an improved adaptive variance mutation method and clustering immune network are proposed in order to enhance
the function of MOPSO and AIN. The global convergence properties and convergence rate of the improved algorithm are
analyzed and described. Finally, the algorithm is applied to optimize the steelmaking process in practice, which reduces
the electric energy consumption, shortens smelting time and improves lifetime of the furnace lining. The result shows the
effectiveness of the algorithm.