廖星,袁景凌,陈旻骋.一种自适应权重的并行PSO快速装箱算法[J].计算机科学,2018,45(3):231-234, 273
一种自适应权重的并行PSO快速装箱算法
Parallel PSO Container Packing Algorithm with Adaptive Weight
投稿时间:2017-06-19  修订日期:2017-08-31
DOI:10.11896/j.issn.1002-137X.2018.03.036
中文关键词:  智能装箱,粒子群优化,自适应权重,并行计算,GPU加速
英文关键词:Intelligent packing,PSO,Adaptive weight,Parallel computing,GPU acceleration
基金项目:本文受国家自然科学基金(61303029),教育部留学回国启动基金([2012]1707),湖北省自然科学基金(2014CFB836)资助
作者单位E-mail
廖星 武汉理工大学计算机科学与技术学院 武汉430070  
袁景凌 武汉理工大学计算机科学与技术学院 武汉430070 yuanjingling@126.com 
陈旻骋 武汉理工大学计算机科学与技术学院 武汉430070 wester589@gmail.com 
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中文摘要:
      随着智能制造时代的到来,生产线后期产品的智能装箱已成为工业生产的重要环节,如何更快速地得到装箱结果对于提高生产效率尤为重要。以快速装箱为目标,文中提出了一种适用于工业生产线的智能化装箱算法。该算法采用自适应权重法改进了粒子群优化算法,相较于标准粒子群优化及遗传等传统启发式算法有更快的收敛速度;并采用GPU加速,实现了高性能的并行计算,大幅加快了计算速度。实验表明,所提算法同样能得到很好的空间利用率, 同时其收敛速度也显著优于传统算法。
英文摘要:
      With the arrival of intelligent manufacturing,the intelligent packing of product or container in the late production line has become an important part of industrial production,and how to get the packing results faster is also important for improving the production efficiency.Mainly aiming at the rapid packing,this paper proposed an intelligent packing algorithm for industrial production line.The algorithm uses the adaptive weight method to improve the particle swarm optimization algorithm,which has a faster convergence rate than the traditional heuristic algorithm,such as standard particle swarm optimization algorithm and genetic algorithm.The calculation speed is greatly accelerated by achieving high performance parallel computing with GPU acceleration.Experiments show that the algorithm proposed in this paper can also get very high space utilization rate,and its convergence speed is faster than the traditional algorithm.
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