多目标拆卸线平衡问题的Pareto遗传模拟退火算法

发布时间：2018-08-05 15:00

【摘要】：针对传统方法求解多目标拆卸线平衡问题时求解结果单一、无法平衡各目标等不足,提出一种基于Pareto解集的多目标遗传模拟退火算法。该算法融合了遗传操作的快速全局搜索能力和模拟退火操作较强的局部搜索能力,对遗传操作的结果进行模拟退火操作,避免了算法陷入局部最优。结合多目标优化问题的特点,改进了模拟退火操作的Metropolis准则。根据拆卸序列之间的Pareto支配关系得到非劣解,并采用拥挤距离评价非劣解,实现了拆卸序列的精英保留,进而将非劣解添加到种群中,加快了算法的收敛速度。基于25项拆卸任务算例,通过与现有的6种单目标算法进行对比,验证了所提算法的有效性,并将所提算法应用于某拆卸线实例中,求得10种平衡方案,结果表明所提算法较Pareto蚁群算法更具优势。
[Abstract]:Aiming at the shortcomings of traditional method in solving the multi-objective disassembly line balance problem, such as single result and unable to balance each target, a multi-objective genetic simulated annealing algorithm based on Pareto solution set is proposed. The algorithm combines the fast global search ability of genetic operation and the strong local search ability of simulated annealing operation. The simulated annealing operation of the result of genetic operation is carried out to avoid the algorithm falling into local optimum. Combined with the characteristics of multi-objective optimization problem, the Metropolis criterion of simulated annealing operation is improved. According to the Pareto dominating relation between disassembly sequences, the noninferior solution is obtained, and the non-inferior solution is evaluated by crowding distance. The elite reservation of disassembly sequence is realized, and then the non-inferior solution is added to the population, which accelerates the convergence speed of the algorithm. Based on 25 disassembly task examples, the effectiveness of the proposed algorithm is verified by comparing it with 6 existing single-objective algorithms. The proposed algorithm is applied to a disassembly line example and 10 balancing schemes are obtained. The results show that the proposed algorithm is superior to the Pareto ant colony algorithm.
【作者单位】：西南交通大学机械工程学院;
【基金】：国家自然科学基金资助项目(51205328,51405403) 教育部人文社会科学研究青年基金资助项目(12YJCZH296) 四川省应用基础研究计划资助项目(2014JY0232)~~
【分类号】：TP18

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