The unpredictable benefits and low operational costs of unmanned aerial vehicles (UAVs) compared to their conventional counterparts caused a tremendous change in commercial and military concepts, and different solutions to problems regarding modern aerial vehicles were tried to further improve flight and job performances. One of the most challenging problems about the UAVs is known as the path planning problem, and a solution should satisfy some objectives related to the enemy anti-air weapons, fuel or battery consumption, and manoeuvre capability of the UAV being operated optimally. Immune plasma algorithm (IP algorithm or IPA) is a recent meta-heuristic optimiser, and its competitive performance has been validated over a set of engineering problems. In this study, a greedy initialiser that is responsible for generating a population of IPA was first introduced. Also, the treatment schema of the IPA was completely redesigned for more robust and detailed search characteristics without requiring either IPA-specific control parameters or their subtle configurations. The new IPA-based path planner, called greedy initialiser IPA (gintIPA), was tested by using three battlefields and 12 test cases belonging to them, and the obtained results were compared with the results of the well-known meta-heuristic techniques. Comparative studies showed that gintIPA is capable of planning more robust and safe UAV paths than other techniques for $91.6$ of all test cases. The proposed greedy initialiser gives a chance to start optimisation with qualified individuals and the newly designed treatment schema improving both exploitation and exploration routines significantly contributes to the gintIPA when outperforming other path planners.

A path being planned for an unmanned aerial vehicle (UAV) or its armed conformation called the unmanned combat aerial vehicle (UCAV) has a critical importance on the flight safety and success of the task including reconnaissance, surveillance, monitoring or destroying. The Back-and-Forth (BaF) algorithm is one of the most recent greedy techniques that executes a heuristic approach for generating two backbone candidates and a combination procedure for bringing advantageous segments of them in order to solve the geometric description of the path planning problem. This study was devoted to a thread-based parallel implementation of the BaF algorithm, also named the multi-threaded BaF (tBaF for short). The threads of the tBaF algorithm increase their search bounds gradually according to the assigned thread indexes and execute the heuristic approach and combination procedure of the BaF for calculating paths. A set of detailed experiments was carried out with the aim of evaluating the performance of the tBaF, and its results were compared with the BaF and 14 other meta-heuristic-based path planners over three battlefield scenarios and their 12 test cases containing the preidentified enemy threats. Comparative studies showed that the different local and global search capabilities of the threads gained with the newly introduced thread-based organisation give a significant contribution to the path planning performance and allow the tBaF to be ranked among the top three planners even though it requires at least from four to eight times less function calls than the tested meta-heuristics.