改进免疫遗传算法求解柔性作业车间调度问题

Flexible Job-shop Scheduling Problem Solved by Improved Immune Genetic Algorithm

针对单目标柔性作业车间调度问题,以最小化最大完工时间为目标建立了生产调度模型,并在此模型的基础上设计了一种收敛速度和求解稳定性均较优的免疫遗传算法。该算法采用三种方式相结合混合策略的种群初始化方式产生初始解,改善了初始种群的形成机制,提高了种群的初始质量和多样性。在基于提高算法搜索能力的基础上提出了抗体浓度调节方式及根据抗体浓度正交自适应调节的交叉算子、变异算子的构造方法。针对免疫遗传算法早熟收敛问题,利用种群分割的思想增加了其多样性,进一步提高了算法的收敛能力。最后,使用MATLAB求解基准算例对算法的性能进行仿真测试,并给出了算例仿真的最优甘特图与收敛图,通过与其他算法的求解结果相比较,验证了该算法有效性和可行性。

Aiming at the single-objective flexible job-shop scheduling problem,the production scheduling model is established with the target of minimum completion time,on which an improved immune genetic algorithm with higher convergence and steadier equation solving ability is devised.This algorithm adopts population initialization with mixed strategy that integrated three methods generated the initial results,optimizing the forming mechanism of the initial population and improving its quality and diversity.The way of adjusting antibody concentration and the construction method of crossover operator and mutation operator that is adjusted by the adaptive orthogonality strategy of antibody concentration are put forward on the basis of improving the algorithm global search ability.For the problem of premature convergence of immune genetic algorithm,its diversity is increased by adopting the idea of population decomposition,so as to further improve the convergence ability of the algorithm.Finally,the performance of the algorithm is simulated by using MATLAB to solve the benchmark example,and the optimal Gantt chart and convergence chart of the simulation are given.Compared with other algorithms,it is verified that the proposed algorithm is effective and feasible.