基于量子粒子群算法的制造网格资源调度问题研究

Research on the manufacturing grid resource scheduling problem based on quantum particle swarm algorithm

对制造网格资源调度问题进行研究,提出了一种收敛速度快、全局性能好、不易陷入局部最优的智能迭代算法-量子粒子群算法来实现对该问题的求解。该算法采用整数编码方式,将网格资源调度问题转化成准连续优化问题,并采用加权目标组合的方式处理多目标条件。最后通过具体实例,对该算法进行了仿真验证,结果表明,在求解制造网格资源调度这类NP-Hard问题时,量子粒子群算法能获得比遗传算法更优的求解效果。

To study the manufacturing grid resource scheduling problem, we use quantum particle swarm optimization（QPSO）to solve this problem, which have the good convergence speed, good performance in searching global optimum and good performance in avoiding trapping in the local optimum. We change the grid resource scheduling problem into a quasi- continuous problem by using a integer coding, and we process multi objective terms in the way of combination the weighted objective. Finally we use a practical analyses to confirm the performance of the method. The results show that QPSO is effective in solving the problem. The results of simulation indicate that QPSO perform better than the genetic algorithm.