摘要
针对遗传算法解决车间作业调度问题时存在早熟收敛的缺点,采用一种新型进化算法——DNA进化算法解决车间作业调度问题.将算法从连续优化问题拓展用于解决离散优化问题,并将其成功地应用于Job shop生产调度.采用了著名的M u th和T hom pson标准问题FT 10进行了验证.仿真结果表明,与遗传算法相比,该算法简单有效,不仅具有很好的求解性能,而且具有更快的收敛速度和全局搜索能力.
Aiming at the limitations Of genetic algorithm such as converging at Iocal optimum, an original evolutionary algorithm named DNA evolutionary algorithm, is used to solve the Job shop scheduling problems. The DNA evolutionary algorithm is extended to solve discrete optimization problems. And the presented algorithm is successfully applied to Job shop scheduling problems. The simulation results for the famous muth and thompson problem FT10× 10 show that the algorithm is quite easy and effective, and not only has rapid convergence ability but also global searching ability compared with genetic algorithm.
出处
《控制与决策》
EI
CSCD
北大核心
2005年第10期1157-1160,共4页
Control and Decision
基金
国家自然科学基金项目(60274043)
上海市科委重大科技攻关项目(04dz11008)
