The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper coo...The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.展开更多
With the increasing energy consumption of computing systems and the growing advocacy for green computing, energy efficiency has become one of the critical challenges in high-performance heterogeneous computing systems...With the increasing energy consumption of computing systems and the growing advocacy for green computing, energy efficiency has become one of the critical challenges in high-performance heterogeneous computing systems. Energy consumption can be reduced by not only hardware design but also software design. In this paper, we propose an energy-aware scheduling algorithm with equalized frequency, called EASEF, for parallel applications on heterogeneous computing systems. The EASEF approach aims to minimize the finish time and overall energy consumption. First, EASEF extracts the set of paths from an application. Then, it reconstructs the application based on the extracted set of paths to achieve a reasonable schedule. Finally, it adopts a progressive way to equalize the frequency of tasks to reduce the total energy consumption of systems. Randomly generated applications and two real-world applications are examined in our experiments. Experimental results show that the EASEF algorithm outperforms two existing algorithms in terms of makespan and energy consumption.展开更多
基金Project(61801495)supported by the National Natural Science Foundation of China
文摘The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.
基金Project supported by the National Natural Science Foundation of China （Nos. 61133005, 61432005, 61370095, 61472124, and 61402400）
文摘With the increasing energy consumption of computing systems and the growing advocacy for green computing, energy efficiency has become one of the critical challenges in high-performance heterogeneous computing systems. Energy consumption can be reduced by not only hardware design but also software design. In this paper, we propose an energy-aware scheduling algorithm with equalized frequency, called EASEF, for parallel applications on heterogeneous computing systems. The EASEF approach aims to minimize the finish time and overall energy consumption. First, EASEF extracts the set of paths from an application. Then, it reconstructs the application based on the extracted set of paths to achieve a reasonable schedule. Finally, it adopts a progressive way to equalize the frequency of tasks to reduce the total energy consumption of systems. Randomly generated applications and two real-world applications are examined in our experiments. Experimental results show that the EASEF algorithm outperforms two existing algorithms in terms of makespan and energy consumption.
