An effective hybrid particle swarm cooperative optimization (HPSCO) algorithm combining simulated annealing method and simplex method is proposed. The main idea is to divide particle swarm into several sub-groups and achieve optimization through cooperativeness of different sub-groups among the groups. The proposed algorithm is tested by benchmark functions and applied to material balance computation (MBC) in alumina production. Results show that HPSCO, with both a better stability and a steady convergence, has faster convergence speed and higher global convergence ability than the single method and the improved particle swarm optimization method. Most importantly, results demonstrate that HPSCO is more feasible and efficient than other algorithms in MBC.