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The problem of decentralized multiple Point of Interests (PoIs) detection andassociated task completion in an unknown environment with multipleresource-constrained and self-interested Unmanned Aerial Vehicles (UAVs) isstudied. The UAVs form several coalitions to efficiently complete the compoundtasks which are impossible to be performed individually. The objectives of suchcoalition formation are to firstly minimize resource consumption in completingthe encountered tasks on time, secondly to enhance the reliability of thecoalitions, and lastly in segregating the most trusted UAVs amid the selfinterested of them. As many previous publications have merely focused onminimizing costs, this study considers a multi-objective optimization coalitionformation problem that considers the three aforementioned objectives. In doingso, a leader-follower- inspired coalition formation algorithm amalgamating thethree objectives to address the problem of the computational complexity ofcoalition formation in large-scale UAV networks is proposed. This algorithmattempts to form the coalitions with minimally exceeding the required resourcesfor the encountered tasks while maximizing the number of completed tasks. Theproposed algorithm is based on Quantum Evolutionary Algorithms(QEA) which are acombination of quantum computing and evolutionary algorithms. Results fromsimulations show that the proposed algorithm significantly outperforms theexisting coalition formation algorithms such as merge-and-split and a famousmulti-objective genetic algorithm called NSGA-II.

Leader-follower based Coalition Formation in Large-scale UAV Networks, A Quantum Evolutionary Approach