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Artificial immune system simulates the architecture and operating pattern of biological immune system,it has been successfully applied in many intelligent computation systems.In this paper,an improved clone selection algorithm is proposed,the algorithm integrates genetic algorithm with immune computing and makes use of reproduction and mutation operator to maintain the diversity and optimization of candidate objects.As an experiment of the clone algorithm,a blind signature scheme with immune optimized parameter is proposed.In the signature scheme,parameters generated with clone selection have relatively higher level of fitness and thus avoids the arbitrary selection of essential parameters.Lastly,we analyze the efficiency and feasibility of immune optimization algorithms with experiment data from the blind signature scheme.The reproduction operator in the algorithm can greatly improve the fitness level of candidate group,while the mutation operator effectively maintains the diversity of candidate individuals.In the experiment,the optimization coefficient (OC) reaches 0.9301 when the clone algorithm is executed just one time.The signature scheme and the experiments show that the improved clone algorithm can effectively improve the efficiency and accuracy of optimization operator in immune system.