The unmanned aerial vehicle (UAV) has drawn increasing attention in recent years, especially in executing tasks such as natural disaster rescue and detection, and battlefield cooperative operations. Task assignment and path planning for multiple UAVs in the above scenarios are essential for successful mission execution. But, effectively balancing tasks to better excavate the potential of UAVs remains a challenge, as well as efficiently generating feasible solutions from the current one in constrained explosive solution spaces with the increase in the scale of optimization problems. This paper proposes an efficient approach for task assignment and path planning with the objective of balancing the tasks among UAVs and achieving satisfactory temporal resolutions. To be specific, we add virtual nodes according to the number of UAVs to the original model of the vehicle routing problem (VRP), thus make it easier to form a solution suitable for heuristic algorithms. Besides, the concept of the universal distance matrix is proposed to transform the temporal constraints to spatial constraints and simplify the programming model. Then, a Swap-and-Judge Simulated Annealing (SJSA) algorithm is therefore proposed to improve the efficiency of generating feasible neighboring solutions. Extensive experimental and comparative studies on different scenarios demonstrate the efficiency of the proposed algorithm compared with the exact algorithm and meta-heuristic algorithms. The results also inspire us about the characteristics of a population-based algorithm in solving combinatorial discrete optimization problems.

Unmanned aerial vehicle (UAV) path planning is crucial in UAV mission fulfillment, with the aim of finding a satisfactory path within affordable time and moderate computation resources. The problem is challenging due to the complexity of the flight environment, especially in three-dimensional scenarios with obstacles. To solve the problem, a hybrid differential symbiotic organisms search (HDSOS) algorithm is proposed by combining the mutation strategy of differential evolution (DE) with the modified strategies of symbiotic organism search (SOS). The proposed algorithm preserves the local search capability of SOS, and at the same time has impressive global search ability. The concept of traction function is put forward and used to improve the efficiency. Moreover, a perturbation strategy is adopted to further enhance the robustness of the algorithm. Extensive simulation experiments and comparative study in two-dimensional and three-dimensional scenarios show the superiority of the proposed algorithm compared with particle swarm optimization (PSO), DE, and SOS algorithm.

High energy consumption in cloud data centers has become one of the main obstacles to green cities, and an urgent problem to be solved. So far, a large number of scheduling algorithms have been developed to reduce energy consumption for executing workflows. However, most existing algorithms have obvious defects in energy and resource efficiency, because they schedule workflow tasks to hosts directly and ignore that the host is so powerful that a single workflow task cannot make full use of its resources. To resolve the issue, a new scheduling architecture is designed for cloud data centers. Then, two principles are derived: one suggests that hybrid scheduling tasks from different workflows is helpful for improving resource utilization; the other one deduces that balancing host weighted square frequencies can minimize total power consumption of active hosts under a given resource requirement. On the basis of the scheduling architecture and the two principles, an oNline schEduling AlgoriThm, namely NEAT, is proposed to schedule dynamic workflows with deadlines. Furthermore, three strategies for dynamically adjusting the available virtual machines (VMs) and active hosts are proposed and integrated into NEAT to improve the energy and resource efficiency for cloud data centers. Finally, the proposed NEAT is compared with three existing algorithms using real-world workflow traces to demonstrate its superior performance with respect to energy and resource efficiency while guaranteeing the timing requirements of workflows. Compared with baseline algorithms, NEAT is capable of reducing energy consumption for cloud data centers by an average of 40% in the range from 34.0% to 45.67%.

The increasingly abundant satellite information resources make new requirements for satellite information supporting missions, and how to effectively and efficiently dispose this information for the support of military or industrial applications becomes a research field that many people are now concerned with. SAIC (Satellite Application Information Chain) can facilitate the seamless connection among kinds of satellite information, thus to a great extent support the applications related to Air Offensive Campaign. However, the problem of work-flow scheduling in SAIC remains an intractable challenge yet to be solved. Drawing inspiration from the research of work-flow scheduling, we propose a dynamic scheduling model with the resource constraint as well as the dynamic scheduling algorithm for multi-workflow in this paper, supported by realistic case studies. By applying the algorithm on scientific calculation workflows, we also test and validate the utility of the algorithm, i.e., in specific circumstances, this algorithm can indeed help to enhance the task scheduling profit, which testifies itself a model that has the expected application value in reality.