一种自适应权重的并行PSO快速装箱算法

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。

Parallel PSO Container Packing Algorithm with Adaptive Weight

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.