摘要
由于量子粒子群优化算法仍有可能会出现早熟现象,因此将变异机制引入量子粒子群优化算法以使算法跳出局部最优并增强其全局搜索能力,并将改进后的量子粒子群优化算法用于求解作业车间调度问题。仿真实例表明,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、粒子群优化算法和量子粒子群优化算法。
Because Quantum-behaved Particle Swarm Optimization（QPSO） algorithm possibly run into prematurity,the mutation mechanism is introduced into QPSO algorithm to escape from local optima and strengthen its global search ability,and the improved QPSO algorithm is applied to solve Job-Shop Scheduling Problem.The simulation results show that this algorithm has better global convergence ability and more rapid convergence,and it is superior to Genetic Algorithm,Particle Swarm Optimization algorithm and QPSO algorithm.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第8期49-52,共4页
Computer Engineering and Applications
基金
国家自然科学基金( the National Natural Science Foundation of China under Grant No.60474030)
关键词
变异机制
作业车间调度
遗传算法
粒子群优化算法
量子粒子群优化算法
mutation mechanism
Job-Shop Scheduling
Genetic Algorithm（GA）
Particle Swarm Optimization algorithm
Quantum-behaved Particle Swarm Optimization algorithm
