Research on Genetic Algorithm Optimization with Fusion Tabu Search Strategy and Its Application in Solving Three-Dimensional Packing Problems

Symmetry is an important principle and characteristic that is prevalent in nature and artificial environments. In the three-dimensional packing problem, leveraging the inherent symmetry of goods and the symmetry of the packing space can enhance packing efficiency and utilization.The three-dimensiona...

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Bibliographic Details
Published in:Symmetry (Basel) 2024-04, Vol.16 (4), p.449
Main Authors: Kang, Zhenjia, Guan, Yong, Wang, Jiake, Chen, Pengzhan
Format: Article
Language:English
Subjects:
Algorithms
Artificial environments
Boxes
Combinatorial analysis
Design
Efficiency
Genetic algorithms
Genetic research
Heuristic
Heuristic methods
improved genetic algorithm
Logistics
Mathematical programming
Methods
Mutation
Neural networks
Optimization
Packing problem
Search algorithms
Search methods
Symmetry
Tabu search
Three dimensional models
three-dimensional packing problem
wall-building method
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Summary:Symmetry is an important principle and characteristic that is prevalent in nature and artificial environments. In the three-dimensional packing problem, leveraging the inherent symmetry of goods and the symmetry of the packing space can enhance packing efficiency and utilization.The three-dimensional packing problem is an NP-hard combinatorial optimization problem in the field of modern logistics, with high computational complexity. This paper proposes an improved genetic algorithm by incorporating a fusion tabu search strategy to address this problem. The algorithm employs a three-dimensional loading mathematical model and utilizes a wall-building method under residual space constraints for stacking goods. Furthermore, adaptation of fitness variation strategy, chromosome adjustment, and tabu search algorithm are introduced to balance the algorithm’s global and local search capabilities, as well as to enhance population diversity and convergence speed. Through testing on benchmark cases such as Bischoff and Ratcliff, the improved algorithm demonstrates an average increase of over 3% in packing space utilization compared to traditional genetic algorithms and other heuristic algorithms, validating its feasibility and effectiveness. The proposed improved genetic algorithm provides new insights for solving three-dimensional packing problems and optimizing logistics loading schedules, offering promising prospects for various applications.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym16040449