In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on pa...In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all devices.PSO-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution.展开更多
The antenna geometry strategy for direction finding （DF） with multiple-input multiple-output （MIMO） radars is studied. One case, usually encountered is practical applications, is consi- dered. For a directional an...The antenna geometry strategy for direction finding （DF） with multiple-input multiple-output （MIMO） radars is studied. One case, usually encountered is practical applications, is consi- dered. For a directional antenna geometry with a prior direction, the trace-optimal （TO） criterion （minimizing the trace） on the av- erage Cramer-Rao bound （CRB） matrix is employed. A qualitative explanation for antenna geometry is provided, which is a combi- natorial optimization problem. In the numerical example section, it is shown that the antenna geometries, designed by the proposed strategy, outperform the representative DF antenna geometries.展开更多
A modified direct optimization method is proposed to solve the optimal multi-revolution transfer with low-thrust between Earth-orbits. First, through parameterizing the control steering angles by costate variables, th...A modified direct optimization method is proposed to solve the optimal multi-revolution transfer with low-thrust between Earth-orbits. First, through parameterizing the control steering angles by costate variables, the search space of free parameters has been decreased. Then, in order to obtain the global optimal solution effectively and robustly, the simulated annealing and penalty function strategies were used to handle the constraints, and a GA/SQP hybrid optimization algorithm was utilized to solve the parameter optimization problem, in which, a feasible suboptimal solution obtained by GA was submitted as an initial parameter set to SQP for refinement. Comparing to the classical direct method, this novel method has fewer free parameters, needs not initial guesses, and has higher computation precision. An optimal-fuel transfer problem from LEO to GEO was taken as an example to validate the proposed approach. The results of simulation indicate that our approach is available to solve the problem of optimal muhi-revolution transfer between Earth-orbits.展开更多
基金The National Natural Science Foundation of China(No.61741102,61471164,61601122)the Fundamental Research Funds for the Central Universities(No.SJLX_160040)
文摘In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all devices.PSO-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution.
基金supported by the National Natural Science Foundation of China(6107211761302142)
文摘The antenna geometry strategy for direction finding （DF） with multiple-input multiple-output （MIMO） radars is studied. One case, usually encountered is practical applications, is consi- dered. For a directional antenna geometry with a prior direction, the trace-optimal （TO） criterion （minimizing the trace） on the av- erage Cramer-Rao bound （CRB） matrix is employed. A qualitative explanation for antenna geometry is provided, which is a combi- natorial optimization problem. In the numerical example section, it is shown that the antenna geometries, designed by the proposed strategy, outperform the representative DF antenna geometries.
基金Sponsored by the National Natural Science Foundation of China(Grant No.10672044)
文摘A modified direct optimization method is proposed to solve the optimal multi-revolution transfer with low-thrust between Earth-orbits. First, through parameterizing the control steering angles by costate variables, the search space of free parameters has been decreased. Then, in order to obtain the global optimal solution effectively and robustly, the simulated annealing and penalty function strategies were used to handle the constraints, and a GA/SQP hybrid optimization algorithm was utilized to solve the parameter optimization problem, in which, a feasible suboptimal solution obtained by GA was submitted as an initial parameter set to SQP for refinement. Comparing to the classical direct method, this novel method has fewer free parameters, needs not initial guesses, and has higher computation precision. An optimal-fuel transfer problem from LEO to GEO was taken as an example to validate the proposed approach. The results of simulation indicate that our approach is available to solve the problem of optimal muhi-revolution transfer between Earth-orbits.
