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The envisaged usage of multiple Unmanned Aerial Vehicles (UAVs) to perform cooperative tasks is a promising concept for future autonomous military systems. An important aspect to make this usage a reality is the solution of the task allocation problem in these cooperative systems. This paper addresses the problem of tasks allocation among agents representing UAVs, considering that the tasks are created by a central entity, in which the decision of which task will be performed by each agent is not decided by this central entity, but by the agents themselves. The assumption that tasks are created by a central entity is a reasonable one, given the way strategic planning is carried up in military operations. To enable the UAVs to have the ability to decide which tasks to perform, concepts from swarm intelligence and multi-agent system approach are used. Heuristic methods are commonly used to solve this problem, but they present drawbacks. For example, many tasks end up not begin performed even if the UAVs have enough resources to execute them. To cope with this problem, this paper proposes three algorithm variants that complement each other to form a new method aiming to increase the amount of performed tasks, so that a better task allocation is achieved. Through experiments in a simulated environment, the proposed method was evaluated, yielding enhanced results for the addressed problem compared to existing methods reported in the literature. [ABSTRACT FROM AUTHOR]

Multi-agent technology is applied in this paper to solve the distribution system service restoration problems. Specifically, switches in the distribution systems are modeled as intelligent agents and organized as local power committees in a distribution system. The advantage of this approach is to utilize the solving ability of a local committee of agents who could coordinate with neighboring committees to achieve an agreement that satisfies the global objective of the power distribution system service restoration problems without relying on a pre-determined centralized optimization algorithm as traditional approaches. The proposed coordination method helps switches in the distributed local power committees to reach a consensus among many proposed service restoration solutions after faults are detected and isolated. We also take the voltage violations into consideration in the search of solutions. Two examples of different power distribution systems are presented to demonstrate how the algorithms of the coordination method can identify the best restoration solution among many proposed local candidate solutions. The simulation results show that the proposed committee-based multi-agent approach yields better performance than the previous methods. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the problem of clustering based procedure for the identification of PieceWise Auto-Regressive eXogenous (PWARX) models is addressed. This problem involves both the estimation of the parameters of the affine sub-models and the hyperplanes defining the partitions of the state-input regression. In fact, we propose the use of the Chiu's clustering algorithm in order to overcome the main drawbacks of the existing methods which are the poor initialization and the presence of outliers. In addition, our approach is able to generate automatically the number of sub-models. Simulation results are presented to illustrate the performance of the proposed method. An application of the developed approach to an olive oil esterification reactor is also suggested in order to validate simulation results. [Copyright &y& Elsevier]