基于自适应灰狼优化算法的柔性作业车间调度问题

Flexible job shop scheduling problem based on adaptive grey wolf optimization algorithm

针对单目标柔性作业车间调度问题(Flexible Job shop Scheduling Problem,FJSP),以优化最大完工时间为目标,提出一种自适应灰狼优化(Adaptive Grey Wolf Optimization,AGWO)算法求解该问题。首先,采用离散整数编码方式以及混合初始化规则生成高质量种群;其次,根据灰狼优化(Grey Wolf Optimization,GWO)算法的社会等级制度,提出一种基于种群规模的自适应社会等级制度分布策略,以提高算法求解速度和稳定性;然后,设计一种新的狼群捕猎和猎物搜索机制,保证种群多样性的同时提高算法的全局探索能力;此外,提出融合基于关键路径和均衡机器负载2种邻域结构的变邻域搜索策略,提高算法的局部搜索能力;最后,通过标准算例验证算法的有效性和可行性。

Aiming at the single-objective Flexible Job shop Scheduling Problem(FJSP)with the objective of optimizing the makespan,and an Adaptive Grey Wolf Optimization(AGWO)algorithm was proposed to solve the problem.A discrete integer encoding method and hybrid initialization rules were used to generate high-quality populations.Secondly,according to the Grey Wolf Optimization(GWO)algorithm social hierarchy,an adaptive social hierarchy distribution strategy based on population size was proposed to improve the speed and stability of the algorithm.Then,a new wolf pack hunting and prey search mechanism was designed to ensure population diversity while improving the algorithm′s global exploration capabilities.In addition,a variable neighborhood search strategy based on two neighborhood structures of critical path and balanced machine load was proposed to improve the local search ability of AGWO algorithm.Finally,the effectiveness and feasibility of AGWO algorithm were verified by standard calculation examples.