[1]黄孔亮,雍正正.一种求解作业车间调度问题的协同进化算法[J].深圳大学学报理工版,2004,21(3):272-275.
 HUANG Kong-liang and YONG Zheng-zheng.A new coevolutionary genetic algorithm for job-shop scheduling problems[J].Journal of Shenzhen University Science and Engineering,2004,21(3):272-275.
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一种求解作业车间调度问题的协同进化算法()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第21卷
期数:
2004年3期
页码:
272-275
栏目:
光电与信息工程
出版日期:
2004-07-30

文章信息/Info

Title:
A new coevolutionary genetic algorithm for job-shop scheduling problems
文章编号:
1000-2618(2004)03-0272-04
作者:
黄孔亮雍正正
深圳大学信息工程学院,深圳 518060
Author(s):
HUANG Kong-liang and YONG Zheng-zheng
College of Information Engineering Shenzhen University, Shenzhen 518060, P. R. China
关键词:
协同进化遗传算法作业车间调度问题
Keywords:
coevolution genetic algorithm job-shop scheduling
分类号:
TP 18
文献标志码:
A
摘要:
提出一种用协同进化遗传算法求解作业车间调度问题的新方法.车间调度问题用传统的启发式算法很难求得最优解.协同进化遗传算法模拟生物界物种之间的竞争、捕食、共生及其相互作用下,各物种协同进化,使整个生态系统由低级向高级进化的过程.协同进化算法与传统的遗传算法相比,不仅加快了算法的收敛速度,且可提高算法的搜索能力,避免算法陷入局部最优. 特殊的交叉操作更使所求得的解都为合法解. 实例证明协同进化遗传算法是行之有效的算法.
Abstract:
A novel genetic algorithm for job-shop problems with coevolutionary model has been presented. Job-shop scheduling is a NP-hard problem. It is demonstrated that coevolutionary genetic algorithm (COGA) simulating the competition of ecosystem is capable of speeding up the computation and finding the best solution easier. An effective crossover operation for operation-based representation is used to guarantee the feasibility of the solution. Simulation results demonstrate the effevtiveness of this proposed algorithm. The optimization performance is improved significantly in comparison with the standard genetic algorithm.

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更新日期/Last Update: 2015-11-06