A multi-population immune plasma algorithm for path planning of unmanned combat aerial vehicle

Autonomous flight of an unmanned aerial vehicle (UAV) or its weaponized variant named unmanned combat aerial vehicle (UCAV) requires a route or path determined carefully by considering the optimization objectives about the enemy threats and fuel consumption of the system being operated. Immune Plasma algorithm (IP algorithm or IPA) is one of the most recent optimization techniques and directly models the fundamental steps of a medical method also used for the COVID-19 disease and known as convalescent or immune plasma treatment. In this study, IP algorithm for which a promising performance has already been validated with a single population was first extended to a multi-population domain supported by a migration schema. Moreover, the usage of the donor as a source of plasma for the treatment operations of a receiver was remodeled. The new variant of the IPA empowered with the multi-population and modified donor usage approach was called Multi-IP algorithm or MULIPA. For investigating the solving capabilities of the MULIPA as a UCAV path planner, different battlefield scenarios and algorithm specific parameter configurations were used. The results obtained by the MULIPA were compared with the results of other meta-heuristic based path planners. The comparative studies between MULIPA and other techniques showed that newly proposed IPA variant is capable of finding more secure and fuel efficient paths for a UCAV system.

Automated summary

Autonomous flight of UAVs and UCAVs requires careful consideration of optimization objectives such as enemy threats and fuel consumption. This study extends the Immune Plasma algorithm to a multi-population domain and remodels the usage of the donor in the treatment operations. The newly proposed Multi-IP algorithm, MULIPA, is capable of finding more secure and fuel efficient paths for UCAV systems compared to other meta-heuristic based path planners.