Discrete Pigeon Inspired Simulated Annealing Algorithm and Contract Net Algorithm based on Multi-objective Optimization for Task Allocation of UAV Formation

Xuzan Liu, Yu Han, Yu Han, Jian Chen, Yi Cao, Shubo Wang

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 into 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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