基于灰色综合关联分析的多目标优化方法

针对现有多目标优化方法存在的搜索性能弱、效率低等问题,提出一种基于灰色综合关联分析的多目标优化方法.该多目标优化方法采用单目标优化算法构建高质量的参考序列,计算参考序列与优化解的目标函数值序列之间的灰色综合关联度,定义基于灰色综合关联度的解支配关系准则,将灰色综合关联度作为多目标优化算法的适应度值.以带顺序相关调整时间的多目标流水车间调度问题作为应用对象,建立总生产成本、最大完工时间、平均流程时间及机器平均闲置时间的多目标函数优化模型.提出基于灰色关联分析的多目标烟花算法,对所建立的多目标优化模型进行优化求解.仿真实验表明,所提出多目标烟花算法的性能优于3种基于不同多目标优化方法的烟花算法及两种经典多目标算法,验证了所提出的多目标优化方法及多目标算法的可行性和有效性.

Multi-objective optimization method based on grey synthetic incidence analysis

Aiming at the problems such as poor search performance and low efficiency in existing multi-objective optimization methods, a multi-objective optimization method based on grey synthetic incidence analysis is proposed. In the proposed multi-objective optimization method, a high quality reference sequence is firstly constructed by a single objective optimization algorithm, the grey synthetic incidence degree between the reference sequence and the objective function value sequence of the optimized solution is calculated, and then the dominance relationship of different solutions based on grey synthetic incidence degree is defined. Finally, the grey synthetic incidence degree is used as the fitness value of the multi-objective optimization algorithm. The multi-objective flow shop scheduling problem with sequence-dependent setup times is taken as the application object, and a multi-objective optimization model including the objectives of total production cost, makespan, mean flow time and machine mean idle time is developed. A multi-objective fireworks algorithm based on grey synthetic incidence analysis is proposed to solve the proposed multi-objective optimization model. The simulation experiment shows that the performance of the proposed multi-objective fireworks algorithm is better than these fireworks algorithms based on the different multi-objective optimization methods and two popular multi-objective algorithms, thus the feasibility and effectiveness of the proposed multi-objective optimization method are verified.