求解差异机器平行批调度的双目标协同蚁群算法  

A Bi-objective Synergy Optimization Algorithm of Ant Colony for Scheduling on Non-identical Parallel Batch Machines

作　　者：贾兆红[1,2] 王燕 张以文 JIA Zhao-Hong;WANG Yan;ZHANG Yi-Wen(School of Computer Science and Technology,Anhui University,Hefei 230601;Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education,Anhui University,Hefei 230601)

机构地区：[1]安徽大学计算机科学与技术学院,合肥230601 [2]安徽大学计算智能与信号处理教育部重点实验室,合肥230601

出　　处：《自动化学报》2020年第6期1121-1135,共15页Acta Automatica Sinica

基　　金：国家自然科学基金(71601001,71671168);中国教育部人文社会科学青年基金会(15YJC630041);安徽省科学基金(1608085MG154);安徽省教育厅自然科学基金(KJ2015A062)资助。

摘　　要：利用用户的偏好信息,提出一种基于蚁群的双目标协同优化算法(Bi-objective synergy ant colony optimization algorithm based on Pareto domination,PDACO)并用于求解平行批处理机调度问题.考虑在一组差异容量并带有不同加工功率的平行批处理机器上,加工带有不同到达时间、尺寸和加工时间的一组工件,以同时最小化最大完工时间和总能耗.偏好向量的引入虽然可以提高算法的收敛性,但会降低解的多样性.为了弥补这一缺陷,在本文所提算法中,利用两个子蚁群分别沿着不同方向,迭代地进行独立和联合搜索.最后,通过大量的仿真实验验证了本文提出算法的有效性.The paper proposed a bi-objective synergy optimization algorithm based on ant colony(PDACO)using preference vectors.It is used to solve the batch scheduling problem.These constraints,a set of jobs with different arrival times,sizes,and processing times on a group of parallel batch processing machines(BPMs)with different capacities and different processing powers,are taken into consideration.The objective is to minimize the maximum completion time and the total energy consumption,simultaneously.Although the preference vector is effective to improve the convergence of algorithm,it will deteriorate the diversity of solutions.In order to reduce the adverse effects of the preference vector,two colonies searching solutions in different directions iteratively use the independent and co-operation search approaches in the proposed algorithm.Finally,through extensive simulation experiments,the validity of the algorithm PDACO proposed in this paper is verified.

关 键 词：协同优化 批处理机 用户偏好 最大完工时间 总能耗 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]