摘要
针对批量流水线调度问题,提出了一种改进的人工蜂群算法来优化最大完成时间。该算法运用NEH方法产生初始解,采用混沌遍历的方法生成新的邻域解。为了跳出局部最优,使用最优解的插入扰动来替换一些连续若干步不能改进的解来提高算法的全局搜索能力。采用自适应的局部搜索加强算法的局部搜索能力。仿真试验表明了所得算法的可行性和高效性。
An Improved Artificial Bee Colony（IABC） algorithm is presented for solving the Lot-streaming Flow Shop Scheduling Problem（LFSP） with the objective of minimizing the maximum completion time,i.e.,makespan.In the proposed IABC algorithm,the famous NEH heuristic is used to produce an initial solution,and the chaos is employed to generate a new candidate.In order to avoid trapping into local optima,the solution not improved in a number of generations in the population is replaced by the perturbation of the best solution found so far.In addition,a self-adaptive local search is presented and imbedded in the IABC algorithm to balance the exploitation and exploration.The computational results show that the IABC algorithm is effective and efficient for the LFSP.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第21期35-38,共4页
Computer Engineering and Applications
基金
国家自然科学基金项目No.60874075
No.70871065
No.60774082
No.60834004
数字制造装备与技术国家重点实验室开放课题(华中科技大学)
中国博士后科学基金项目(No.20070410791)~~
关键词
批量流水线调度
最大完成时间
人工蜂群算法
微粒群优化
局部搜索
lot-streaming flow shop scheduling
maximum completion time
artificial bee colony algorithm
particle swarm optimization
local search
