Nowadays naphtha pyrolysis is the most important process for ethylene production, which can bring along another important monomer, namely propylene. The demand of both the ethylene and propylene has recently increased dramatically and naphtha pyrolysis is indispensable to satisfy the demand of both crucial products simultaneously, resulting in a typical multi-objective optimization problem. The nondominated sorting genetic algorithm (NSGA-II). which has been successfully applied to many multi-objective optimization problems. cannot efficiently generate the Pareto set which spreads as widely as the true Pareto front in a limited time, meanwhile, its convergence process is rather slow and could not meet the speed requirement when used for the complicated industrial problem mentioned above. To efficiently solve the multi-objective optimization problem of the industrial complicated chemical processes, this paper first proposed a new parallel hybrid multi-objective optimization algorithm combing NSGA-II with SQP (Successive Quadratic Programming) used to improve the efficiency of the NSGA-II and the quality of the Pareto-optimal set. Then the multi-objective operation optimization model of naphtha pyrolysis was established, and at last the application of the proposed algorithm to improve the performance of an industrial naphtha pyrolysis process was presented and analyzed. (c) 2008 Elsevier Ltd. All rights reserved.