基于退火选择微粒群算法的车间调度问题研究
张敏行，熊瑞平，常敏
（四川大学 制造科学与工程学院，四川 成都 610065）
摘要：针对车间调度问题研究的不足，以及标准微粒群算法只能求解无约束问题和容易陷入局部最优的缺陷，提出一种退火选择微粒群算法。通过构造混合罚函数的方式对目标函数进行优化；采用一种动态权重策略，并与模拟退火算法以及遗传算法的选择过程相结合，有效避免陷入局部最优，提高了算法的寻优能力。将ASPSO算法应用于实例仿真，得到了较好的结果，证明了算法的可行性和有效性。
关键词：微粒群算法；混合罚函数；惯性权重；三角函数算子；模拟退火；局部搜索
中图分类号：F423.1 文献标志码：A doi：10.3969/j.issn.1006-0316.2016.05.014
文章编号：1006－0316 (2016) 05－0059－05
Research of the job shop scheduling problem based on the ASPSO algorithm
ZHANG Minxing，XIONG Ruiping，CHANG Min
( School of Manufacturing Science and Engineering, Sichuan University, Chengdu 610065, China )
Abstract：Annealing selection particle swarm optimization algorithm (ASPSO) is put forward in this paper for the Job Shop Scheduling Problem. For only unconstrained optimization problems can be solved with the Standard Particle Swarm Optimization algorithm (PSO), the method of constructing penalty functions is proposed. However, because of the disadvantage of local optimum drawback in the search of PSO, a dynamic weighting strategy is presented, which is combined with simulated annealing algorithm and Genetic selection process, and the ability of the algorithm optimization is improved. The Algorithm is applied into simulation, and had achieved a good result and the feasibility and effectiveness of the algorithm is proved.
Key words：particle swarm optimization；mixed penalty function；inertia weight；trigonometric operator； simulated annealing；local search
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收稿日期：2015－10－26
作者简介：张敏行（1990－），男，安徽安庆人，硕士研究生，主要研究方向为机械制造及其自动化。
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