Recently particle swarm optimization (PSO) has been achieved more attention, but the basic PSO is easy plunged into local optima. This paper proposed an improvement to basic PSO, named MPSO, in which mutation operator was added as genetic algorithm. The mutation operator can mutate the population, and then help the particles escape from local optima. Experiments on five benchmark functions showed the PSO with mutation operator not only enlarged particles' explore range, but also boosted the algorithm's convergence.

With the fast development of virtual instrument application technology, more and more functional parameters can be set through the software methods. Currently, the successful adjustments of parameter settings is tightly linked with the knowledge of instrumentation and basic principles related to specific tests. However, it is difficult for some end users to deal with this kind of advanced operations. By adopting the Particle Swarm Optimization (PSO) Algorithm, the adaptive set and calibration of instrument parameters can be achieved by software algorithm with computational intelligence. Simulations and experiments showed adaptive parameter calibrating method based on the PSO can significantly enhance the effectiveness of debug, maintenance, and software independence of virtual instrument and test system.