摘要
文中提出一种基于分布估计算法(Estimation of Distribution Algorithm,EDA)的多目标优化算法,用于求解带序相关设置时间的绿色流水线调度问题,优化目标为最小化最大完工时间和总电价。首先,初始解均通过随机产生的方式以保持种群的多样性和分散性;其次,统计非劣解集中优良解的信息并通过概率矩阵对其进行学习并保留;同时,设计一种自适应学习速率来控制优良解信息在整个算法搜索过程中的引导作用;然后,构建自学习的局部搜索策略对非劣解进行深度探索;最后,仿真实验和算法对比表明,文中所提方法能够有效求解此问题,并具有良好稳定性。
A multi-objective optimization algorithm is proposed based on the Estimation of Distribution Algorithm(EDA),to solve the green flow shop scheduling problem with sequence dependent setup time,to minimize the maximum completion time and the total electricity price to reach the optimization goal.Firstly,the initial solutions are randomly generated to maintain the diversity and dispersion of the population.Secondly,the information of the good solutions in the non-inferior solution set is learned and retained through the probability matrix;At the same time,an adaptive learning rate is designed to control the good solution information plays a guiding role in the entire algorithm search process.Then,a self-learning local search strategy is constructed to explore non-inferior solutions in depth.Finally,the effectiveness of the proposed method is verified through simulation experiments and algorithm comparison.
作者
董钰明
胡蓉
姚友杰
DONG Yu-ming;HU Rong;YAO You-jie(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China;Yunnan Kunming Ship Design&Research Institute Co.,Ltd.,Kunming 650051,China)
出处
《信息技术》
2021年第2期1-6,共6页
Information Technology
基金
国家自然科学基金资助项目(51665025,61963022)。
关键词
带序相关设置时间
流水车间调度问题
分布估计算法
分时电价
sequence dependent setup times
flow shop scheduling problem
Estimation of Distribution Algorithm(EDA)
time of use(TOU)
