反向人工蜂群算法求解混合流水车间调度问题

Opposite artificial bee colony algorithm for hybrid flow shop scheduling problem

针对以最小化最大完工时间为优化目标的混合流水车间调度问题,提出一种融合反向学习策略的反向人工蜂群算法求解该问题。首先,根据混合流水车间调度问题的特点,建立了对应的数学模型和仿真优化模型;其次,在寻优过程中为了避免陷入局部最优,分别在种群初始化、雇佣蜂和观察蜂三个阶段引入了反向学习策略,采用两点间逆序策略和元素交换策略加快寻优速度,并采用精英保优策略保留最优解;最后,选取2个实例和21个不同规模的benchmark算例进行仿真实验,通过与相关算法的实验结果进行对比分析,验证了所提算法能有效求解此类问题。

Aiming at the hybrid flow shop scheduling problem with the objective of minimizing the makespan,this paper proposed an opposite artificial bee colony algorithm with opposition-based learning to solve the problem.Firstly,according to the characteristics of hybrid flow shop scheduling problem,this paper established the corresponding mathematical model and simulation optimization model.Secondly,in order to avoid the problem of local optima in the process of optimization,this paper added the opposition-based learning strategy in the three stages of population initialization,employment bee and observation bee,used the reverse order strategy between two points and the element exchange strategy to speed up the optimization,then used the elitist preservation strategy to retain the optimal solution.Finally,the simulation experiment selected two examples and 21 benchmark problems of different scales.The results of simulation experiment verifies that proposed algorithm is effective in solving such problems by comparing with the experimental of related algorithms.