A Discrete Bat Algorithm for Disassembly Sequence Planning

被引：7
|
作者：
Jiao Q. [1 ]
Xu D. [1 ]
机构：
[1] Department of Arms Engineering, Academy of Armored Force Engineering, Beijing
来源：
Journal of Shanghai Jiaotong University (Science) | 2018年 / 23卷 / 2期
关键词：
bat algorithm (BA); differential mutation BA (DMBA); disassembly sequence planning (DSP); discrete BA (DBA); fitness function model (FFM); genetic algorithm (GA); particle swarm optimization (PSO) algorithm;
D O I：
10.1007/s12204-018-1937-6
中图分类号：
学科分类号：
摘要：
Based on the bat algorithm (BA), this paper proposes a discrete BA (DBA) approach to optimize the disassembly sequence planning (DSP) problem, for the purpose of obtaining an optimum disassembly sequence (ODS) of a product with a high degree of automation and guiding maintenance operation. The BA for solving continuous problems is introduced, and combining with mathematical formulations, the BA is reformed to be the DBA for DSP problems. The fitness function model (FFM) is built to evaluate the quality of disassembly sequences. The optimization performance of the DBA is tested and verified by an application case, and the DBA is compared with the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and differential mutation BA (DMBA). Numerical experiments show that the proposed DBA has a better optimization capability and provides more accurate solutions than the other three algorithms. © 2018, Shanghai Jiaotong University and Springer-Verlag GmbH Germany, part of Springer Nature.
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页码：276 / 285
页数：9
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