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The global positioning system is the most popular outdoor positioning system; however, it is inapplicable to indoor environments. In this study, we created a hybrid algorithm, which applies an artificial neural network to resolve the problems of indoor positioning and a swarm-intelligence algorithm to perform the time-consuming task of adjusting parameters. Experiment results demonstrate that the positioning accuracy of the proposed hybrid algorithm is equivalent to the exhaustive method with search times that are far shorter.