求解Job-shop调度问题的遗传蚁群算法  

Genetic ant colony algorithm for Job-shop scheduling problem

作　　者：吴宇明[1] 徐从富[1] 

机构地区：[1]浙江大学人工智能研究所

出　　处：《计算机应用研究》2010年第9期3247-3249,3282,共4页Application Research of Computers

基　　金：国家自然科学基金资助项目(60970081);国家“863”计划资助项目(2007AA01Z197)

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。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.

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 

分 类 号：TP301[自动化与计算机技术—计算机系统结构] TP18[自动化与计算机技术—计算机科学与技术]