Abstract
Sensor is a sort of important monitoring resources and plays an irreplaceable role in the modern battlefield. Multi-sensor scheduling optimization is a problem of theoretical and practical significance. In order to monitor the multi-target with time windows effectively, this paper presents a multi-sensor dynamic scheduling model and demonstrates its reasonableness. Based on the model, we adopt a modified Ant Colony Optimization (ACO) algorithm with local optimization method to find optimal solutions, and conduct several experiments under different scenarios. The results show that more targets are monitored effectively in each solution, therefore the modified ACO algorithm has better performance than basic ACO algorithm in scheduling optimization.
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Huang, H., Zhang, J., Ran, X., Lv, W. (2013). Modified Ant Colony Optimization Algorithm for the Multi-Sensor Dynamic Scheduling. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38524-7_7
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DOI: https://doi.org/10.1007/978-3-642-38524-7_7
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