Abstract
Unmanned Aerial Vehicles (UAV) was introduced after World War II. In 1980’s UAV consider as important weapon system. Initially UAV needs initial position and target position. In this paper bat algorithm is proposed with mixed objective constraints which helps in directing the UAV. The process is initialized by generating the initial population of bat. Then by updating the population size and generation of bat the fitness value with minimum frequency is found that helps to avoid convergence among UAV. Finally the evaluation which gives minimum frequency is considered as optimal solution.
Highlights
Unmanned Aerial Vehicle (UAV) is shortly called military aircraft
The PSO is otherwise called as random search algorithm which helps in solving UAV path planning [11]
The ACO algorithm is based on ant colony behaviour to optimize path planning problems [12]
Summary
Unmanned Aerial Vehicle (UAV) is shortly called military aircraft This mainly used in war and during disaster situation and helps in weather monitoring [1,2]. Various algorithm are used to solve UAV problem[5,6] such as particle swarm optimization algorithm [7], ant colony optimization algorithm [8], pigeon-inspired optimization algorithm (PIO) [9], fruit fly optimization algorithm (FOA) [10]. The PSO is otherwise called as random search algorithm which helps in solving UAV path planning [11]. The ACO algorithm is based on ant colony behaviour to optimize path planning problems [12]. With quantum behaved theory perform in UAV path planning. This paper includes the UAV path planning problem management and proposed bat algorithm in UAV path planning
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