求解job shop调度问题的量子粒子群优化算法*

针对粒子群优化算法搜索空间有限、容易出现早熟现象的缺陷，提出将量子粒子群优化算法用于求解作业车间调度问题。求解时，将每个调度按照一定的规则编码为一个矩阵，并以此矩阵作为算法中的粒子；然后根据调度目标确定目标函数，并按照量子粒子群优化算法的进化规则在调度空间内搜索最优解。仿真实例结果证明，该算法具有良好的全局收敛性能和快捷的收敛速度，调度效果优于遗传算法和粒子群优化算法。

Quantum behaved particle swarm optimization forsolving job shop scheduling problem

Dealing with such disadvantages of PSO algorithm as finite sampling space, being easy to run into prematurity,QPSO algorithm was proposed to be applied to solve job shop scheduling problem (JSSP). During the scheduling process, obeying to some particular regulations, every scheduling was encoded into a matrix, and this matrix was regarded as a particle in QPSO algorithm; the objective function was determined based on the objective of scheduling. According to evolution formulae of QPSO algorithm, the scheduling space was searched for the global optimization. The simulation results show that this algorithm has better global convergence ability and more rapid convergence, and it is superior to genetic algorithm (GA) and PSO algorithm.