Route Planning Based on Genetic Algorithm

Lin Li,Yuhua Zhang

doi:10.5539/jmr.v10n2p122

Abstract

This paper mainly deals with the planning of aviation route and needs to determine the model to find out the shortest path. In this paper, we combine the methods of simulated annealing and genetic algorithm, and obtained the optimal solution method. Firstly, Genetic Algorithm (GA) uses the modified circle algorithm to find some feasible solutions to the approximate initial population, and then transforms them through simulated and crossover operations. This paper also introduces the aircraft fuel consumption model and the cubical smoothing algorithm with five-point approximation to reduce the aircraft fuel consumption and parts loss. The simulation results show that the accuracy of the route planning based on genetic algorithm is higher, while consumes less fuel and takes less sharp turns.

Highlights

Route planning is to find the optimal path for moving objects from the starting point to the target point under certain constraints, satisfying certain performance indexes and certain constraints
This paper introduces the aircraft fuel consumption model and the cubical smoothing algorithm with five-point approximation to reduce the aircraft fuel consumption and parts loss
Genetic algorithm can be widely applied to all kinds of problems, its main feature is that it operates directly with the structure object

Summary

Introduction

Route planning is to find the optimal path for moving objects from the starting point to the target point under certain constraints, satisfying certain performance indexes and certain constraints. UAV route planning is one of the key technologies of virtual simulation training system. It mainly looks for the route from the starting point to the destination of tasks, under the constraints of scheduled tasks, threat distribution, fuel economy, etc. It plans in a dynamic, uncertain and real-time environment, which based on the terrain and enemy information. (Wei Shui, 2011:574-576) In this paper, the problem of route planning based on genetic algorithm is proposed to simulate the requirements of the actual battlefield environment. The superiority of genetic algorithm in applying such problems is demonstrated

Methods

Results

Conclusion