不同支配关系的多目标算法的柔性作业调度

Application of multi-objective algorithm based on different dominance relation in FMC

摘要:

为了提高多目标进化算法所获得解的质量,研究者做了大量的研究,传统的基于Pareto支配关系的多目标进化算法具有一定的局限性。本文利用不同的支配关系与NSGA-II(Non-dominated Sorting Genetic Algorithm)算法相结合,对单机器人搬运的柔性作业车间调度的多目标优化问题进行求解,通过实验比较分析了不同方法在多目标优化问题求解中的优劣性。本文以NSGA-II为框架结合Lorenz支配关系和CDAS(Control Dominance Area of Solutions)支配关系并与传统的基于Pareto支配关系的NSGA-II三种算法去研究同一优化调度问题,发现基于Lorenz支配关系和CDAS支配关系的优化算法比基于传统的Pareto支配关系的优化算法的效果更佳。

Abstract:

In order to improve the quality of solution obtained by multi-objective evolutionary algorithm, researchers have done a lot of research. The traditional multi-objective evolutionary algorithm based on Pareto dominance relationship has certain limitations. This paper combined different domination relations with NSGA-II(Non-dominated Sorting Genetic Algorithm)to solve the multi-objective optimization problem of flexible job shop scheduling carried by a single robot, and compared and analyzed the advantages and disadvantages of different methods in solving high-dimensional multi-objective optimization problem through experiments. This paper takes NSGA-II as the framework, combines Lorenz dominant relation and CDAS(Control Dominance Area of Solutions)dominant relation, and the traditional NSGA-II based on Pareto dominant relation studies the same optimal scheduling problem, and finds that the optimization algorithm based on Lorenz dominant relation and CDAS dominant relation has better effect than that based on the traditional Pareto dominant relation.