渡线
进化算法
排列(音乐)
计算机科学
启发式
数学优化
算法
人口
突变
数学
机器学习
人工智能
物理
人口学
社会学
声学
生物化学
化学
基因
作者
Shuai Shao,Ye Tian,Luchen Wang,Shangshang Yang,Panpan Zhang,Xingyi Zhang
标识
DOI:10.1109/docs60977.2023.10294929
摘要
The car sequencing problem (CSP) in assembly lines has been a significant focus for automobile manufacturers over several decades. Although various optimization methods have been devoted to solving it, most existing work only handled simple CSPs with a few variables and naive objectives, unable to obtain diverse optimal solutions meeting the complicated requirements in real-world scenarios. Therefore, this paper formulates CSPs into a large-scale combinatorial many-objective optimization problem, and solves it by developing a multi-objective evolutionary algorithm based on permutation group. The proposed algorithm suggests permutation group-based crossover and mutation operators to preserve the excellent genes of parents and enhance the search ability. Moreover, by extracting heuristic information from the current population, a probability matrix is established to efficiently repair solutions. To evaluate the performance of the proposed algorithm, empirical comparisons are carried out on nine real-world datasets having up to 1000 variables. The experimental results demonstrate that the proposed algorithm has significant advantages over several state-of-the-art evolutionary algorithms.
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