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Source localization using acoustic sensor networks has been drawing a lot of research interest recently. In a sensor network, there are a large number of inexpensive sensors which are densely deployed in a region of interest. This dense deployment enables accurate intensity (energy)-based target localization. The maximum likelihood is the predominant objective which leads to a variety of source localization approaches. However, the investigation on the energy-based localization for multiple sources has been very rare. The corresponding robust and efficient algorithms are still being pursued by researchers nowadays. In this paper, we would like to combat the energy-based multiple-source localization problem. We propose two new algorithms, namely, alternating projection algorithm and expectation-maximization algorithm, which can combat the energy-based localization problem for multiple sources. Furthermore, we derive the Cramer-Rao lower bound for these two new methods. Through Monte Carlo simulations and theoretical analysis, we also compare the robustness and the computational complexity of these two algorithms.