AN IMPROVED ADAPTIVE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR JOB-SHOP SCHEDULING PROBLEM
This paper presents an improved adaptive particle swarm optimization algorithm (IAPSO) which is inspired from hormone modulation mechanism for solving the minimum makespan problem of job shop scheduling problem (JSP). The environment around swarms and incretion factors are used to modify the updating equations of particle swarm, and the performance of particle swarm optimization is improved. The computational results validate the effectiveness of the proposed IAPSO, which can not only find optimal or close-to-optimal solutions but can also obtain both better and more robust results than the existing PSO algorithms reported recently in the literature. By employing IAPSO, machines can be used more efficiently, which means tasks can be allocated appropriately, production efficiency can be improved, and the production cycle can be shortened efficiently.
Job-shop scheduling problem (JSP) Hormone modulation mechanism Improved adaptive particle swarm optimization algorithm (IAPSO) minimum makespan
Wenbin Gu Dunbing Tang Kun Zheng
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics andAstronautics, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics andAstronautics,
国际会议
北京
英文
407-412
2010-11-23（万方平台首次上网日期，不代表论文的发表时间）