The multi-UAV task allocation problem can be divided into two components: optimising UAV resource allocation and developing an optimal execution plan. Existing single-population algorithms often get trapped in local optima and require improved accuracy. Although multi-population algorithms perform better, they introduce higher complexity, significantly increasing running time. This paper proposes a Two-Stage Multi-Population Wolf Pack Algorithm (2SMPWPA) to address these issues. This algorithm innovatively splits the task allocation problem into two stages: the initial stage focuses on optimising UAV resource utilisation. In contrast, the subsequent stage focuses on optimising the execution plans for the existing UAV resources. Furthermore, the algorithm categorises the population into a leader group and two normal groups, where the leader group consists of elite individuals from the ordinary groups. To ensure the outstanding individuals in the normal groups have adequate computational resources, a population competition mechanism is introduced to dynamically adjust the size of each sub-population based on their average contribution to the optimal solution. To prevent the ‘big eats small’ scenario, the algorithm incorporates population protection and migration mechanisms to maintain diversity. Additionally, a population communication mechanism is implemented to preserve ‘vitality’ during the later iterations, preventing the algorithm from converging to local optima. Comparative experiments demonstrate that the 2SMPWPA significantly outperforms recent algorithms regarding solution accuracy, effectively addressing the trade-off between solution precision and running time.

G-Network queueing network models, and in particular the random neural network (RNN), are useful tools for decision making in complex systems, due to their ability to learn from measurements in real time, and in turn provide real-time decisions regarding resource and task allocation. In particular, the RNN has led to the design of the cognitive packet network (CPN) decision tool for the routing of packets in the Internet, and for task allocation in the Cloud. Thus in this paper, we present recent research on how to dynamically create the means for quality of service (QoS) to end users of the Internet and in the Cloud. The approach is based on adapting the decisions so as to benefit users as the conditions in the Internet and in Cloud servers vary due to changing traffic and workload. We present an overview of the algorithms that were designed based on the RNN, and also detail the experimental results that were obtained in three areas: (i) traffic routing for real-time applications, which have strict QoS constraints; (ii) routing approaches, which operate at the overlay level without affecting the Internet infrastructure; and (iii) the routing of tasks across servers in the Cloud through the Internet.

This paper investigates task allocation for multiple robots by applying the game theory-based negotiation approach. Based on the initial task allocation using a contract net-based approach, a new method to select the negotiation robots and construct the negotiation set is proposed by employing the utility functions. A negotiation mechanism suitable for the decentralized task allocation is also presented. Then, a game theory-based negotiation strategy is proposed to achieve the Pareto-optimal solution for the task reallocation. Extensive simulation results are provided to show that the task allocation solutions after the negotiation are better than the initial contract net-based allocation. In addition, experimental results are further presented to show the effectiveness of the approach presented.

In this paper, a dynamic task allocation and controller design methodology for cooperative robot teams is presented. Fuzzy-logic-based utility functions are derived to quantify each robot's ability to perform a task. These utility functions are used to allocate tasks in real time through a limited lookahead control methodology partially based on the basic principles of discrete event supervisory control theory. The proposed controller design methodology accommodates flexibility in task assignments, robot coordination, and tolerance to robot failures and repairs. Implementation details of the proposed methodology are demonstrated through a warehouse patrolling case study.