基于免疫遗传算法求解多目标柔性作业调度问题

Solving Multi-objective Flexible Job Shop Scheduling Problem Based on Immune Genetic Algorithm

针对柔性作业车间调度问题,提出一种改进的免疫遗传混合算法。该算法使用新的初始化方式产生初始解,然后设计合理的自适应交叉变异机制产生新抗体,再通过免疫接种和免疫记忆等操作优化新的抗体,将免疫算法和遗传算法相结合可以提高算法的全局搜索能力,避免遗传算法陷入早熟收敛的问题。最后使用MATLAB编程求解基准算例来验证所提算法的有效性,再将算法应用于其他文献中实际生产企业的车间调度问题,得到更多的最优解且结果更优,证明了该算法的实用性。

To solve flexible job shop scheduling problem,an improved immune genetic hybrid algorithm was proposed.This paper proposes a new initialization method,then design rational adaptive crossover and mutation mechanism to produce new antibodies,and combine vaccination with immune memory to optimize new antibodies.Combining the immune algorithm with the genetic algorithm can improve the algorithm′s global search ability.Finally using MATLB programming language and adopting the internationally used benchmark examples verify the effectiveness of the proposed algorithm,and then the algorithm is applied to actual production enterprise′s workshop scheduling problem,which can get more optimal solutions and better results,these results can verify the practicability of the algorithm.