Best Individual Guided Immune Plasma Algorithm on Solving Path Planning Problem of Unmanned Combat Aerial Vehicles

Abstract

Unmanned aerial vehicles (UAVs) and their variants equipped with sophisticated weapon systems called unmanned combat aerial vehicles (UCAVs) have completely changed the classical war strategies and concept of military operations. For guaranteeing the autonomous flight safety and success of the task being performed by these modern aerial vehicles, a path must be determined optimally after considering some kinematic constrains, existence of enemy threats, fuel or battery limitations. Immune plasma algorithm (IP algorithm or IPA) inspired by the implementation steps of a medical method gained popularity with the COVID-19 and known as convalescent or plasma treatment is one of the most recent intelligent optimization or meta-heuristic techniques. In this study, plasma treatment procedure of the IPA was changed with a newly introduced approach called the best individual guidance for short BIG that is based on using the most qualified solution found by the algorithm and three different donors when collecting plasma and BIGIPA was developed as a novel UCAV path planner. For investigating the path planning capabilities of the BIGIPA, a set of detailed experiments was carried out by using different battlefield configurations and assigning various constants to the control parameters such as population size and number of receivers and then obtained results were compared with the results of other path planners based on well-known meta-heuristic algorithms. Experimental studies showed that introduced treatment procedure gives a significant contribution to the convergence performance and qualities of the final solutions especially for the test cases with relatively high dimensionalities and BIGIPA calculates more promising, flight efficient, and safe UCAV paths compared to the tested algorithms.