改进粒子群算法在网格资源分配中的优化

Optimization of grid resource allocation using improved particle swarm optimization algorithm

针对并行网格任务的资源分配问题,提出了一种基于并行粒子子群优化的分配算法。该算法引入效用函数,反映网格任务的偏好和目标,利用乘子法转化约束条件,导出适应度函数。最后通过粒子子群的并行寻优过程,得到资源分配的最优解。仿真实验表明了该算法的有效性,且在任务较多的情况下,优化结果好于传统粒子群算法。

To solve the problem of grid resource allocation for parallel tasks,an allocation algorithm based on improved particle swarm optimization was proposed.Utility function was introduced to reveal preferences and objectives of grid tasks.Then particle fitness function was derived by multiplier method.After parallel searching of particle in each sub-swarm,an optimal scenario for grid resource allocation was produced.Simulation experiments demonstrated effectivness of the algorithm.The results showed that the proposed algorithm outperformed standard particle swarm optimization in terms of task execution time and cost.