Zheng S.-M.,Operational Experiment Center |
Gao Z.-N.,Operational Experiment Center |
Wei W.,Operational Experiment Center |
Miao Z.,PLA University of Science and Technology |
Shao R.-M.,Shenyang Artillery Academy of PLA
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | Year: 2012
Aiming at the dynamic characteristic of grid, to overcome the shortcomings of genetic algorithms which easily get a local optimum solution, a cloud-based genetic algorithm (CGA) for grid task scheduling is proposed. CGA is based on both the idea of GA and the properties of randomness and stable tendency of a normal cloud model. In this algorithm, Y-conditional cloud generator is used for the crossover operator, and basic cloud generator is used for the mutation operator. CGA can optimizes the solution with genetic algorithm based on cloud-model, ascertains the oriental scenario for scheduling, and improves on the arithmetic operators of population initialization, select, crossover, mutation and reinsertion in the process of task scheduling. The experiment validates the feasibility, validity, and practicality of the algorithm.