Abstract
In [1], we modeled the problem of safe site visitation sequencing for an unmanned reconnaissance aerial vehicle (URAV) in graph-theoretic language. Our goal was to uncover several alternative flight paths along which the URAV can fly safely, no more than once, to all sites of interest. We offered a solution to a simpler abstraction of this problem using GAs – if we could not uncover any Hamiltonian cycles we reported maximally long paths between any two vertices. This work reports two orthogonal extensions of [1] – a. In addition to possibly reporting Hamiltonian cycles, the GA reports several alternative long paths that begin at the airbase. b. A randomized version of the gene migration scheme is used. The randomization does not appear to affect the quality of the best plan. However it raises the average quality of the best k plans. We give a modified gene encoding scheme and report simulation results.
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Agarwal, A., Lim, MH., Chew, C.Y., Poo, T.K., Er, M.J., Leong, Y.K. (2004). Solution to the Fixed Airbase Problem for Autonomous URAV Site Visitation Sequencing. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_99
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DOI: https://doi.org/10.1007/978-3-540-24855-2_99
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