To solve the problem of multiple attributes value optimization in the upper stage solid propellant rocket engines of a launch vehicle, parameter-cost models of motor parts were established and an improved pareto genetic algorithms(IPGA) that combines the NSGA-�� with local search algorithm was presented. Simulation test shows that the convergence of IPGA is better than that of NSGA-��. Setting the maximum terminal speed increment of the rocket motor and its manufacture cost as objective functions, Pareto optimal sets were obtained by using IPGA which motor case materials are APMOC and carbon respectively. Adapting the deal point method, an inflexion point of cost- effect ratio in Pareto optimal setswas gained. Setting this point as trade-off solution, the calculation results show that the effective load of the launch vehicle was increased by 7.6% and cost of the motor was decreased.
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