Chaotic genetic algorithm (CGA) for path planning of UAVs (unmanned air vehicles)

We propose CGA to overcome the limitations unavoidable when using standard GA to do path planning of UAVs; these limitations, caused by the enormity of search space, include low convergence rate and frequently obtaining local optimum rather than the desired global optimum. We explain the CGA in detail in the paper. As compared with standard GA, our CGA is better in three respects; (1) searching space is enlarged through chaotic search, (2) searching efficiency is improved, (3) global optimum is ensured. Finally we give a numerical simulation example for path planning of an UAV, whose results for both our CGA and standard GA are shown; these results show preliminarily that our CGA improves the optimization efficiency and that the path planned by our CGA can avoid threats better.