Authors:
Xuzan Liu
1
;
Yu Han
2
;
3
;
Jian Chen
1
;
Yi Cao
1
and
Shubo Wang
1
Affiliations:
1
College of Engineering, China Agricultural University, 17 Qinghua East Rd., Beijing, China
;
2
College of Water Resources & Civil Engineering, China Agricultural University, 17 Qinghua East Rd., Beijing, China
;
3
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Rd., Wuhan 430079, China
Keyword(s):
Task Allocation, Multi-objective Optimization, Multi-Unmanned Aerial Vehicles, Discrete Pigeon-inspired Optimization-Simulated Annealing Algorithm, Contract Net Algorithm.
Abstract:
In this paper, a mathematical model of multi-objective optimization under complex constraints is established to solve the task allocation problem. Among them, the constraint indexes include UAV quantity constraint and fuel consumption constraint; the optimization objectives include the gain, loss and fuel consumption. Discrete Pigeon Inspired Optimization-Simulated Annealing (DPIO-SA) algorithm is proposed to solve this problem. The experimental results show that while the total fitness reaches the optimum, the gain is the largest, the loss and fuel consumption are the smallest. After running the algorithm 30 times. The number of times that DPIO-SA reaches the global optimum is 15, while DPIO is 2. In addition, the average value of DPIO-SA after stabilization is 13.5% larger than that of DPIO. Both prove that after joining SA, the algorithm is easier to reach the global extremum. The Contract Net Algorithm (CNA) is adopted to solve the task scheduling problem. The UAVs are divided in
to tenderer UAV, potential bidder UAVs, bidder UAVs and winner UAV. After network communication, suitable bidder UAV is found to replace tenderer UAV to perform the task. Experimental results show that the algorithm has good applicability.
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