A New Parallel Artificial Bee Colony Algorithm for Path Planning of Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAV) and their military specialization powered by air-to-air or air-to-ground weapon systems and called unmanned combat aerial vehicles (UCAV) are used frequently for the critical operations due to the relatively low production and maintenance costs. The success of a task performed by these mentioned modern aerial vehicles has a direct relationship with the planned path. However, path planning is an NP-hard problem that should be solved optimally by considering the enemy threats, fuel consumption and some constraints defined for a UAV or UCAV. In this study, a new multi-colony based implementation of ABC algorithm in which onlooker bee phase is also modified was introduced for solving UAV or UCAV path planning problem. The paths calculated by the newly introduced multi-colony based implementation of ABC algorithm were compared with the paths found by other meta-heuristic algorithms. Comparative studies showed that the colonies assigned to different cores of a processor working simultaneously improve the solving performance of the ABC algorithm and help proposed path planner for outperforming other tested techniques.