UAV routing with genetic algorithm based matheuristic for border securitymissions

Abstract

In recent years, Unmanned Aerial Vehicles (UAVs) are a good alternative for theproblem of ensuring the security of the borders of the countries. UAVs arepreferred because of their speed, ease of use, being able to observe many points atthe same time, and being more cost-effective in total compared to other securitytools. This study is dealt with the problem of the use of UAVs for the security ofthe Turkey-Syria borderline which becomes sensitive in recent years and theproblem is modeled as a UAV routing problem. To solve the problem, a GeneticAlgorithm Based Matheuristic (GABM) approach has been developed and 12scenarios have been created covering the departure bases, daily patrol numbers, and ranges of UAVs. GABM finds the minimum number of UAVs to use inscenarios with the help of a GA run first and tries to find the optimal routes forthese UAVs. If GABM can find an optimal route for the determined UAV number,it decreases the UAV number and tries to solve the problem again. GABMproposes a hybrid approach in which a metaheuristic with a mathematical modelworks together and the metaheuristic sets an upper limit for the number of UAVsin the model. In computational studies, when compared GA with GABM it is seenthat GABM has obtained good results and decreased the utilized number of UAVs (up to 400%) and their flight distances (up to 85.99%) for the problem in veryshort CPU times (max. 122.17 s. for GA and max. 46.39 s. for GABM in additionto GA).