5篇
您的检索式：作者名="WANNENG SHU"
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| 1 | A Parallel Genetic Simulated Annealing Hybrid Algorithm for Task Scheduling显示文摘In this paper combined with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm (PGSAHA) and applied to solve task scheduling problem in grid computing .It first generates a new group of individuals through genetic operation such as reproduction, crossover, mutation, etc, and than simulated anneals independently all the generated individuals respectively. When the temperature in the process of cooling no longer falls, the result is the optimal solution on the whole. From the analysis and experiment result, it is concluded that this algorithm is superior to genetic algorithm and simulated annealing. | SHU Wanneng ZHENG Shijue | 2006 | Wuhan University Journal of Natural Sciences2006，11，5： | 11 |
| 2 | Joint offloading strategy based on quantum particle swarm optimization for MEC-enabled vehicular networks显示文摘With the development of the mobile communication technology,a wide variety of envisioned intelligent transportation systems have emerged and put forward more stringent requirements for vehicular communications.Most of computation-intensive and power-hungry applications result in a large amount of energy consumption and computation costs,which bring great challenges to the on-board system.It is necessary to exploit traffic offloading and scheduling in vehicular networks to ensure the Quality of Experience(QoE).In this paper,a joint offloading strategy based on quantum particle swarm optimization for the Mobile Edge Computing(MEC)enabled vehicular networks is presented.To minimize the delay cost and energy consumption,a task execution optimization model is formulated to assign the task to the available service nodes,which includes the service vehicles and the nearby Road Side Units(RSUs).For the task offloading process via Vehicle to Vehicle(V2V)communication,a vehicle selection algorithm is introduced to obtain an optimal offloading decision sequence.Next,an improved quantum particle swarm optimization algorithm for joint offloading is proposed to optimize the task delay and energy consumption.To maintain the diversity of the population,the crossover operator is introduced to exchange information among individuals.Besides,the crossover probability is defined to improve the search ability and convergence speed of the algorithm.Meanwhile,an adaptive shrinkage expansion factor is designed to improve the local search accuracy in the later iterations.Simulation results show that the proposed joint offloading strategy can effectively reduce the system overhead and the task completion delay under different system parameters. | Wanneng Shu Yan Li | 2023 | Digital Communications and Networks2023，9，1： | 2 |
| 3 | An improved genetic simulated annealing algorithm applied to task scheduling in grid computing显示文摘 | WANNENG SHU SHIJUE ZHENG LI GAO | 2006 | Dynamics of Continuous Discrete and Impulsive Systems-Series B-applications Algorithms2006，，12： | 1 |
| 4 | Min-Min Chromosome Genetic Algorithm for Load Balancing in Grid Computing显示文摘 | Shu Wanneng Wang Jianqing | 2009 | International Journal of Distributed Sensor Networks2009，5，1： | 1 |
| 5 | An improved genetic simulated annealing algorithm applied to task scheduling in grid computing显示文摘 | Shu Wanneng Zheng Shijue Gao Li | 2006 | Dynamics of Continuous Discrete and Impulsive Systems-series B-applications & algorithms2006，，： | 1 |
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