MULTI-UAV Task Allocation Based on Improved Genetic Algorithm

Xueli Wu,Ran Zhen,Yanan Yin,Xiaojing Wu,Fanhua Meng,Lei Xu

doi:10.1109/access.2021.3097094

Abstract

The path length of multiple unmanned aerial vehicle (multi-UAV) has a certain impact on the task allocation of multi-UAV. In order to improve the efficiency of multi-UAV and reduce the loss of multi-UAV during the process of performing tasks, this paper takes the path length as one of the influencing factors of the evaluation function. The UAV path length, UAV performance, and task characteristics are taken as the influencing factors of multi-UAV task allocation evaluation function. In addition, in order to improve the efficiency of genetic algorithm (GA) in solving multi-UAV task allocation problem, this paper proposes a fusion genetic algorithm based on improved simulated annealing (ISAFGA). In order to improve the population diversity of GA, the second selection operation of GA is carried out and the improved simulated annealing algorithm (SA) is used in the second selection operation. The threshold is set to improve the acceptance criteria of new solutions in SA, and then the promotion of secondary selection operation on population diversity is improved. The simulation results showed that the improved algorithm could improve the diversity of the population and improve the global search ability, and verified the effectiveness of the improved algorithm.

Highlights

With the development of science and technology, unmanned aerial vehicle (UAV) has the advantages of convenience, quickness, and small size
Different from the above reference, in order to improve the efficiency of solving the multi-UAV task allocation sequence, this paper proposes an improved simulated annealing fusion genetic algorithm (ISAFGA) for multi- UAV task allocation
In the whole process of executing the task, in order to reduce the time for the algorithm to get the task allocation order, the off-line mode is used to calculate the multi-UAV path length

Summary

INTRODUCTION

With the development of science and technology, unmanned aerial vehicle (UAV) has the advantages of convenience, quickness, and small size. Multi UAV task allocation belongs to NP-hard combinatorial optimization problem [4]. Reference [14] shows the improved SA algorithm was introduced into discrete PSO, which solved the problem that discrete PSO was easy to fall into local minimum. X. Wu et al.: MULTI-UAV Task Allocation Based on Improved GA the search ability of GA by using SA, and solve the problem that GA is prone to local optimization [15]. Reference [18] shows that a double chromosome multi mutation GA was proposed to solve the problem of large-scale reconnaissance task allocation. Different from the above reference, in order to improve the efficiency of solving the multi-UAV task allocation sequence, this paper proposes an improved simulated annealing fusion genetic algorithm (ISAFGA) for multi- UAV task allocation.

BASIC MODEL

CHARACTERISTICS OF UAV

Findings

SELECTION OPERATION