Computer Science ›› 2018, Vol. 45 ›› Issue (3): 231-234, 273.doi: 10.11896/j.issn.1002-137X.2018.03.036

Previous Articles     Next Articles

Parallel PSO Container Packing Algorithm with Adaptive Weight

LIAO Xing, YUAN Jing-ling and CHEN Min-cheng   

  • Online:2018-03-15 Published:2018-11-13

Abstract: 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.

Key words: Intelligent packing,PSO,Adaptive weight,Parallel computing,GPU acceleration

[1] NGOI B K A,TAY M L,CHUA E S.Applying spatial representation techniques to the container packing problem[J].International Journal of Production Research,1994,32(1):111-123.
[2] BISCHOFF E E,RATCLIFF B S W.Issues in the development of approaches to container loading[J].Omega,1995,23(4):377-390.
[3] DAVIES A P,BISCHOFF E E.Weight distribution considerations in container loading[J].European Journal of Operational Research,1999,114(3):509-527.
[4] GEHRING H,BORTFELDT A.A genetic algorithm for solving the container loading problem[J].International Transactions in Operational Research,1997,4(5/6):401-418.
[5] BORTFELDT A,GEHRING H.A hybrid genetic algorithm for the container loading problem[J].European Journal of Operational Research,2001,131(1):143-161.
[6] MOURA A,OLIVEIRA J F.A GRASP approach to the container loading problem[J].IEEE Intelligent Systems,2005,20(4):50-57.
[7] BISCHOFF E E.Three-dimensional packing of items with limi-ted load bearing strength[J].European Journal of Operational Research,2006,168(3):952-966.
[8] ZHANG D F,PENG Y,ZHANG L L.A Multi-Layer Heuristic Search Algorithm for Three Dimensional Container Loading Problem[J].Chinese Journal of Computers,2012,35(12):2553-2561.(in Chinese) 张德富,彭煜,张丽丽.求解三维装箱问题的多层启发式搜索算法[J].计算机学报,2012,35(12):2553-2561.
[9] LIU S,ZHU F H,LV Y S,et al.A Heuristic Orthogonal Binary Tree Search Algorithm for Three Dimensional Container Loa-ding Problem[J].Chinese Journal of Computers,2015,38(8):1530-1543.(in Chinese) 刘胜,朱凤华,吕宜生,等.求解三维装箱问题的启发式正交二叉树搜索算法[J].计算机学报,2015,38(8):1530-1543.
[10] WEI Z,WU L,GE F Z,et al.Hybrid PSO Algorithm Based on Memetic Framework[J].Pattern Recognition and Artificial Intelligence,2012,25(2):213-219.(in Chinese) 魏臻,吴雷,葛方振,等.基于Memetic框架的混合粒子群算法[J].模式识别与人工智能,2012,25(2):213-219.
[11] REN P.Research and Applications of Particle Swarm Optimization[D].Shenyang:Northeastern University,2006.(in Chinese) 任苹.粒子群优化算法研究与应用[D].沈阳:东北大学,2006.
[12] MENG F,HUANG T A,XIE Z C.New Simplified ParticleSwarm Optimization and Its Application to Container Loading Problem[J].Science Technology and Engineering,2013,13(31):9214-9218.(in Chinese) 孟非,黄太安,解志斌.一种简化粒子群算法及在三维装箱问题中的应用[J].科学技术与工程,2013,13(31):9214-9218.
[13] ZHANG D F,PENG Y,ZHU W X,et al.A Hybrid Simulated Annealing Algorithm for the Three-Dimensional Packing Problem[J].Chinese Journal of Computers,2009,32(11):2147-2156.(in Chinese) 张德富,彭煜,朱文兴,等.求解三维装箱问题的混合模拟退火算法[J].计算机学报,2009,32(11):2147-2156.
[14] AO Y C,SHI Y B,ZHANG W,et al.Improved Particle Swarm Optimization with Adaptive Inertia Weight[J].Journal of University of Electronic Science and Technology of China,2014,43(6):874-880.(in Chinese) 敖永才,师奕兵,张伟,等.自适应惯性权重的改进粒子群算法[J].电子科技大学学报,2014,43(6):874-880.
[15] CAI Y,LI G Y,WANG H.Research and implementation of pa-rallel particle swarm optimization based on CUDA[J].Application Research of Computers,2013,30(8):2415-2418.(in Chinese) 蔡勇,李光耀,王琥.基于CUDA的并行粒子群优化算法的设计与实现[J].计算机应用研究,2013,30(8):2415-2418.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .