《计算机应用研究》|Application Research of Computers

求解Job-shop调度问题的遗传蚁群算法

Genetic ant colony algorithm for Job-shop scheduling problem

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作者 吴宇明,徐从富
机构 浙江大学 人工智能研究所,杭州 310027
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文章编号 1001-3695(2010)09-3247-03
DOI 10.3969/j.issn.1001-3695.2010.09.011
摘要 描述了Job-shop调度问题,研究遗传算法和蚁群算法在解决Job-shop问题中的优点和不足,融合遗传算法和蚁群算法设计了遗传蚁群算法以求解Job-shop调度问题,并对算法进行了仿真实验,通过与遗传算法、蚁群算法及已有的遗传算法和蚁群算法的融合算法结果的对比,验证了该算法的有效性。
关键词 Job-shop调度问题;遗传算法;蚁群算法;遗传算法与蚁群算法的融合;遗传蚁群算法
基金项目 国家自然科学基金资助项目(60970081)
国家“863”计划资助项目(2007AA01Z197)
本文URL http://www.arocmag.com/article/1001-3695(2010)09-3247-03.html
英文标题 Genetic ant colony algorithm for Job-shop scheduling problem
作者英文名 WU Yu-ming, XU Cong-fu
机构英文名 Institute of Artificial Intelligence, Zhejiang University, Hangzhou 310027, China
英文摘要 This paper addressed the problem of Job-shop scheduling, and analyzed the advantage and disadvantage of GA and ACO in solving the Job-shop scheduling problem. By combining GA and ACO, proposed a novel GACA to solve the above problem. Simulation results are better compared with those obtained from GA, ACO and GAAA. It shows that the novel algorithm GACA is feasible and efficient.
英文关键词 Job-shop scheduling; genetic algorithm (GA); ant colony optimization (ACO); genetic algorithm-ant algorithm(GAAA); genetic ant colony algorithm (GACA)
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页码 3247-3249
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文献标志码 A