Abstract
As a smart combination of particle swarm optimization (PSO) and sequential number-theoretic optimization (SNTO), a new hybrid PSO–SNTO algorithm is proposed to handle the initialization dependence of basic PSO algorithm. We then apply the hybrid algorithm to the acoustic source localization problem in sensor networks, which is modeled as a maximum likelihood estimation problem. Furthermore, a heuristic method based on virtual force is used to direct the particles of PSO to the global optimum, which can efficiently speed up the algorithm convergence. Simulation results demonstrate that the hybrid algorithm can achieve robust convergence with sophisticated estimation performance, and the convergence rate can be largely enhanced with the assistance of the force-directed heuristics.
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