| 基于QPSO算法的作业车间调度问题的研究 |
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| 引用本文： | 冯斌,石锦风,孙俊.基于QPSO算法的作业车间调度问题的研究[J].计算机工程与设计,2007,28(23):5690-5693,5786. |
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| 作者姓名： | 冯斌 石锦风 孙俊 |
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| 作者单位： | 江南大学,信息工程学院,江苏,无锡,214122 |
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| 摘 要： | 针对现行的遗传算法存在进化速度过慢和过早收敛的局限,以及粒子群优化算法搜索空间有限、容易陷入局部最优点的缺陷,提出将一种基于量子行为的粒子群优化算法应用于作业车间调度问题.将该问题中的每个调度组成一个多维向量,以此向量作为量子粒子群优化算法中的粒子进行进化,由此在解空间内搜索最优解.实例仿真结果表明,该算法收敛速度快、全局收敛性能好,可以得到比遗传算法、粒子群优化算法更佳的调度效果,证明了算法的有效性.
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| 关 键 词： | 遗传算法 群体智能算法 粒子群优化算法 量子粒子群优化算法 作业车间调度问题 算法收敛速度 作业 车间调度问题 研究 algorithm based scheduling problem 有效性 效果 收敛性能 仿真结果 最优解 搜索空间 解空间 粒子群优化算法 量子行为 多维向量 组成 应用 缺陷 |
| 文章编号： | 1000-7024（2007）23-5690-04 |
| 收稿时间： | 2006-12-25 |
| 修稿时间： | 2006年12月25 |
Research on job-shop scheduling problem based on QPSO algorithm |
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| FENG Bin,SHI Jin-feng,SUN Jun.Research on job-shop scheduling problem based on QPSO algorithm[J].Computer Engineering and Design,2007,28(23):5690-5693,5786. |
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| Authors: | FENG Bin SHI Jin-feng SUN Jun |
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| Abstract: | Coping with such limitations of genetic algorithm as too slower evolutionary speed, premature convergence, and such dis- advantages of particle swarm optimization algorithm as finite sampling space, being easy to run into local optima, quantum-behaved particle swarm optimization is proposed to be applied to job-shop scheduling problem. A multidimensional vector composed of every scheduling regarded as a particle in this algorithm to evolve. Then, the sampling space is searched for the global optima. The simulation results show that this algorithm has more rapid convergence and better global convergence ability, and it is superior to genetic algorithm and particle swarm optimization algorithm, and it is also verified that the proposed algorithm is effective and feasible. |
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| Keywords: | genetic algorithm(GA) swarm intelligence algorithm particle swarm optimization(PSO)algorithm quantumbehaved particle swarm optimization(QPSO)algorithm job-shop scheduling problem(JSSP) |
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