置换流水车间调度问题的中心引力优化算法求解

发布时间：2018-11-07 17:23

【摘要】：目前求解置换流水车间调度问题的智能优化算法都是随机型优化方法,存在的一个问题是解的稳定性较差。针对该问题,本文给出一种确定型智能优化算法——中心引力优化算法的求解方法。为处理基本中心引力优化算法对初始解选择要求高的问题,利用低偏差序列生成初始解,提高初始解质量;利用加速度和位置迭代方程更新解的状态;利用两位置交换排序法进行局部搜索,提高算法的优化性能。采用置换流水车间调度问题标准测试算例进行数值实验,并和基本中心引力优化算法、NEH启发式算法、微粒群优化算法和萤火虫算法进行比较。结果表明该算法不仅具有更好的解的稳定性,而且具有更高的计算精度,为置换流水车间调度问题的求解提供了一种可行有效的方法。
[Abstract]:At present, the intelligent optimization algorithms for the replacement flow shop scheduling problem are all stochastic optimization methods, one of the problems is the poor stability of the solution. In order to solve this problem, this paper presents a definite intelligent optimization algorithm, which is called central gravity optimization algorithm. In order to deal with the problem that the selection of the initial solution is required by the basic central gravity optimization algorithm, the initial solution is generated by using the low deviation sequence to improve the quality of the initial solution, and the state of the solution is updated by the iterative equations of acceleration and position. The local search is carried out by using the two-position exchange sorting method to improve the optimization performance of the algorithm. A standard test example of permutation flow shop scheduling problem is used to carry out numerical experiments, and the numerical results are compared with basic central gravity optimization algorithm, NEH heuristic algorithm, particle swarm optimization algorithm and firefly algorithm. The results show that the algorithm not only has better stability of solution, but also has higher computational accuracy. It provides a feasible and effective method for solving the replacement flow shop scheduling problem.
【作者单位】：上海理工大学管理学院;
【基金】：国家自然科学基金项目(71401106) 教育部人文社会科学研究规划基金项目(16YJA630037) 上海市高原学科建设项目上海市“科技创新行动计划”软科学研究重点项目(17692109400) 上海高校青年教师培养资助计划项目(ZZsl15018) 上海理工大学国家级培育青年基金项目(16HJPY-QN15) 上海理工大学博士科研启动经费项目(1D-15-303-005)
【分类号】：TB497;TP18

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