Sequence Transfer-Based Particle Swarm Optimization Algorithm for Irregular Packing Problems

Jie Fang,Pan Liu,Yunqing Rao,Xusheng Zhao

doi:10.1109/access.2021.3114331

Abstract

The two-dimensional (2D) irregular packing problem is a classical optimization problem with NP-hard characteristics and has high computational complexity. To date, packing problems have generally been solved by artificial experience and heuristic algorithms. However, these algorithms are not highly efficient and the excellent cases cannot be preserved, which both time and economic costs are high. Inspire by transfer learning and considering the characteristics of 2D irregular packing problems, we propose a sequence transfer-based particle swarm optimization algorithm (ST-PSO) to solve the multi-constraint packing problem. A piece-matching strategy based on an improved shape context algorithm, and a piece-sequencing generation strategy for transferring the packing sequence are developed for particle swarm optimization(PSO) initialization. In the process of PSO, an adaptive adjustment strategy is used with an improved positioning strategy to adjust the packing position of the pieces. The results indicate that this method can robustly, quickly, and efficiently achieve the packing of 2D irregular pieces. Compared with the data prior to transfer, the ST-PSO can inherit and transfer the historical packing sequence in less time and retain or exceed the actual packing data onto the samples. This algorithm could be applied to industrial applications to reduce waste, packing time, and production costs.

Highlights

Packing optimization problems generally exist in the production process of plate metals, steel structures, ships, clothing, leather and paper products, glass, and other industries
It is worth noting that, the optimal packing sequence will be stored in the information storage module (ISM) after the final packing task is completed, that is to say, the historically optimal packing sequence is stored in the ISM, which can be used as a source task data set
A novel fast particle swarm optimization (PSO) algorithm incorporating sequence transfer is developed for 2D irregular piece packing optimization

Summary

INTRODUCTION

Packing optimization problems generally exist in the production process of plate metals, steel structures, ships, clothing, leather and paper products, glass, and other industries. Each time the search starts from the initial information state of the pieces, and does not consider whether similar solutions could be obtained based on previous packing problems This leads to long calculation time, low convergence speed, and a utilization rate of packing that does not meet the actual packing requirements. Yang et al [23] recently combined PSO with a transfer learning algorithm to propose an evolutionary optimization framework based on transfer learning of similar historical information [25]. They proposed a fast PSO algorithm guided by transfer learning to solve large-scale traveling salesman problems (TSP). Piece packing experiments based on real examples and shapes are performed to verify the effectiveness of the algorithm for industrial packing applications

PROBLEM STATEMENT

NOVEL POSITIONING STRATEGY BASED ON NFP

METHOD OF SEQUENCE TRANSFER FOR 2D IRREGULAR PIECE PACKING PROBLEMS

PROBLEM INSTANCES

CONCLUSIONS