Use of a quantum genetic algorithm for coalition formation in large-scale UAV networks

Abstract

Task allocation among a network of heterogeneous resource-constrained Unmanned Aerial Vehicles (UAVs) in an unknown and remote environment is still a challenging problem noting the limited available information about highly dynamic environment, lack of continuous and reliable communication network, and the limited energy and resources available at the UAVs. One solution for this such allocation problem is to form several efficient coalitions of the UAVs, where a complex task is assigned to a group of agents (i.e., a coalition) carrying the required resources/capabilities to perform this task. In this paper, inspired by Quantum Evolutionary Algorithms, we propose a leader-follower coalition formation algorithm in a large-scale UAV network to form the best possible coalitions of agents to accomplish the detected tasks in an unknown environment. Three main objectives have been considered in this coalition formation: (i) minimizing resource consumption in completing the assigned tasks on time; (ii) enhancing the reliability of the coalitions; and (iii) considering the most trustworthy UAVs amid the self-interested UAVs in forming the coalitions. The simulation results demonstrate the superior performances of the proposed model in different scenarios with large number of UAVs compared to existing coalition formation algorithms such as merge-and-split and a famous multi-objective genetic algorithm called NSGA-II.11DISTRIBUTION A. Approved for public release: distribution unlimited. Case Number: 88ABW-2018-0096. Dated 10 Jan 2018.22An earlier version of this work was presented at 2018 INFOCOM Workshop on WISARN: Wireless Sensor, Robot and UAV Networks, Honolulu, HI.