The Mixed No-Idle Flow-shop Scheduling Problem(MNIFSP)is an extension of flow-shop scheduling,which has practical significance and application prospects in production scheduling.To improve the efficacy of solving the ...The Mixed No-Idle Flow-shop Scheduling Problem(MNIFSP)is an extension of flow-shop scheduling,which has practical significance and application prospects in production scheduling.To improve the efficacy of solving the complicated multiobjective MNIFSP,a MultiDirection Update(MDU)based Multiobjective Particle Swarm Optimization(MDU-MoPSO)is proposed in this study.For the biobjective optimization problem of the MNIFSP with minimization of makespan and total processing time,the MDU strategy divides particles into three subgroups according to a hybrid selection mechanism.Each subgroup prefers one convergence direction.Two subgroups are individually close to the two edge areas of the Pareto Front(PF)and serve two objectives,whereas the other one approaches the central area of the PF,preferring the two objectives at the same time.The MDU-MoPSO adopts a job sequence representation method and an exchange sequence-based particle update operation,which can better reflect the characteristics of sequence differences among particles.The MDU-MoPSO updates the particle in multiple directions and interacts in each direction,which speeds up the convergence while maintaining a good distribution performance.The experimental results and comparison of six classical evolutionary algorithms for various benchmark problems demonstrate the effectiveness of the proposed algorithm.展开更多
利用到达角(Angel Of Arrival,AOA)进行目标定位是被动监测领域广泛采用的技术之一.然而,在多基站多目标环境中,通常难以直接获得AOA量测数据间的关联关系,因此需要在目标定位前进行有效的量测数据关联.本文针对AOA量测数据的关联问题,...利用到达角(Angel Of Arrival,AOA)进行目标定位是被动监测领域广泛采用的技术之一.然而,在多基站多目标环境中,通常难以直接获得AOA量测数据间的关联关系,因此需要在目标定位前进行有效的量测数据关联.本文针对AOA量测数据的关联问题,提出了一种基于多向次序关联的AOA量测数据关联方法.该方法首先构建了一种用于描述数据间关联程度的代价函数,并利用雅克比方法估计误差分量的方差.其次结合分配算法和寻优思想,分别计算局部关联方向和基站的关联次序,最终得到关联结果.实验验证了本文方法对密集目标和随机目标量测数据关联的有效性.展开更多
基金This work was partly supported by the National Natural Science Foundation of China(No.61772173)the Science and Technology Research Project of Henan Province(No.202102210131)+1 种基金the Innovative Funds Plan of Henan University of Technology(No.2020ZKCJ02)the Grant-in-Aid for Scientific Research(C)of Japan Society of Promotion of Science(No.19K12148).
文摘The Mixed No-Idle Flow-shop Scheduling Problem(MNIFSP)is an extension of flow-shop scheduling,which has practical significance and application prospects in production scheduling.To improve the efficacy of solving the complicated multiobjective MNIFSP,a MultiDirection Update(MDU)based Multiobjective Particle Swarm Optimization(MDU-MoPSO)is proposed in this study.For the biobjective optimization problem of the MNIFSP with minimization of makespan and total processing time,the MDU strategy divides particles into three subgroups according to a hybrid selection mechanism.Each subgroup prefers one convergence direction.Two subgroups are individually close to the two edge areas of the Pareto Front(PF)and serve two objectives,whereas the other one approaches the central area of the PF,preferring the two objectives at the same time.The MDU-MoPSO adopts a job sequence representation method and an exchange sequence-based particle update operation,which can better reflect the characteristics of sequence differences among particles.The MDU-MoPSO updates the particle in multiple directions and interacts in each direction,which speeds up the convergence while maintaining a good distribution performance.The experimental results and comparison of six classical evolutionary algorithms for various benchmark problems demonstrate the effectiveness of the proposed algorithm.
文摘利用到达角(Angel Of Arrival,AOA)进行目标定位是被动监测领域广泛采用的技术之一.然而,在多基站多目标环境中,通常难以直接获得AOA量测数据间的关联关系,因此需要在目标定位前进行有效的量测数据关联.本文针对AOA量测数据的关联问题,提出了一种基于多向次序关联的AOA量测数据关联方法.该方法首先构建了一种用于描述数据间关联程度的代价函数,并利用雅克比方法估计误差分量的方差.其次结合分配算法和寻优思想,分别计算局部关联方向和基站的关联次序,最终得到关联结果.实验验证了本文方法对密集目标和随机目标量测数据关联的有效性.
