改进自适应遗传算法在柔性车间调度的应用

Application of Adaptive Improvement Genetic Algorithm in Flexible Workshop Scheduling

在柔性作业车间调度问题中,传统遗传算法的搜索广度低且极易陷入局部最优解,因此,本文对传统遗传算法进行自适应改进,使其能够更好地解决调度问题。本文依据柔性作业车间调度问题的特点,建立相应的数学模型,在遗传操作上使用自适应改进的交叉算子和变异算子,使其能够在迭代过程中根据种群内每个独立个体的适应度值进行非线性调整,对最优解实行精英保留策略,使完工时间最短。改进策略提高了算法全局搜索能力,加快了收敛速度,增加了种群多样性,更快求得柔性作业车间调度问题的最优解。最后,通过测试车间调度问题中的LA01算例,该算法得到了目前的LA01算例的最优解,证明了该算法具有一定的高效性与可行性。

In the flexible job-shop scheduling pro blem,the traditional genetic algorithm has a low search breadth and is prone to fall into local optima,therefore,this paper proposes an adaptive improvement of the traditional genetic algorithm to better solve the scheduling problem.Based on the characteristics of the flexible job-shop scheduling problem,this paper establishes a corresponding mathematical model,and uses adaptive improved crossover and mutation operators in the genetic operations,which can be non-linearly adjusted according to the fitness value of each independent individual in the population during the iteration process.Elite retention strategy is also introduced to minimize the completion time.The improvement strategy enhances the global search capability of the algorithm,thereby accelerating convergence speed,increasing population diversity,and achieving faster optimal solution of the flexible job-shop scheduling problem.Finally,through testing the LA01 case in the workshop scheduling problem,the algorithm obtained the optimal solution for the LA01 case,demonstrating that the algorithm has a certain degree of efficiency and feasibility.