Quantum inspired monarch butterfly optimisation for UCAV path planning navigation problem

As a complicated high-dimensional optimisation problem, path planning navigation problem for uninhabited combat air vehicles (UCAV) is to obtain a shortest safe flight route with different types of constrains under complicated combating environments. Monarch butterfly optimisation (MBO) is a highly promising swarm intelligence algorithm. Since then, though it has successfully solved several challenging problems, MBO may be trapped into local optima sometimes. In order to improve the performance of MBO, quantum computation is firstly incorporated into the basic MBO algorithm and a new quantum inspired MBO algorithm is then proposed called QMBO. In QMBO, a certain number of the worst butterflies are updated by quantum operators. In this paper, the UCAV path planning navigation problem is modelled into an optimisation problem and then, its optimal path can be obtained by the proposed QMBO algorithm. In addition, B-spline curves are utilised to further smoothen the obtained path and make it more feasible for UCAV. The UCAV path obtained by QMBO is compared with the basic MBO and the experimental results show that QMBO can find much shorter path than MBO.