摘要
针对车间调度问题,提出一种新的基于粒子群优化和模拟退火的混合算法.该算法将问题规模作为启发式信息,通过对模拟退火算法引入新的邻域搜索机制——多粒度搜索,并加入选择优化和淘汰更新机制,提高了算法的自适应性和自学习能力,降低了粒子群算法陷入局部最优的可能性.实验结果表明,该算法在最优解的求解能力上优于其他算法.
For j ob shop scheduling problem,we proposed a new particle swarm optimization and simulated annealing based algorithm,in which we made use of the information of the problem itself, added a new neighborhood search strategy （multi-granularity）in simulated annealing search,and introduced selection optimization and updating parts into the original particle swarm optimization algorithm.All the adjustments make our algorithm more adaptive,improve the ability of self-learning,and reduce the possibility of trapping in the local best. Our algorithm was tested on different scale benchmarks and compared with recently proposed algorithms.The experimental results show that our algorithm is more adaptive and efficient than the other three algorithms.
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2014年第1期93-97,共5页
Journal of Jilin University:Science Edition
基金
国家自然科学基金(批准号:60873148
60973089
61170314
61272208)
吉林省科技发展计划项目(批准号:20071106)
关键词
车间调度
粒子群优化
自适应
自学习
j ob shop scheduling
particle swarm optimization
adaptive
self-learning
