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This paper addresses the selection of sensors for target localization and tracking under nonlinear and nonGaussian dynamic conditions. We have used the Posterior Cramer-Rao lower Bound (PCRB) as the performance-based optimization criteria because of its built-in capability to produce online estimation performance predictions, a "must" for high maneuverable targets or when slow-response sensors are used. In this paper, we analyze, and compare, three optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and a new discrete-variant of the cuckoo search algorithm (CS). Finally, we propose local-search versions of the previous optimization algorithms that provide a significant reduction of the computation time. (C) 2014 Elsevier B.V. All rights reserved.