Enhanced Harris Hawks Optimization Algorithm with Integrated Tabu Search for 3D Bin Packing Problems: Application in Logistics Industries

The Harris Hawks Optimization (HHO) algorithm has proven to be an effective metaheuristic tool for addressing complex optimization challenges. This study outlines significant modifications and enhancements proposed for the standard HHO algorithm to improve its performance on complex three-dimensional bin packing problems (3D-BPP). The proposed modifications introduce a hybrid metaheuristic framework that synergistically merges the global search capabilities of an enhanced HHO with the local intensification strength of Tabu Search (TS), along with several additional algorithmic improvements. In this study, we present an enhanced Harris Hawks Optimization algorithm integrated with Tabu Search for 3D bin packing problems (EHHITS). This problem is particularly significant in logistics and warehousing, where improving space utilization directly contributes to cost reduction. The primary objective is to maximize the occupied volume of the container by efficiently packing smaller rectangular items (boxes) without overlaps. The proposed EHHITS algorithm employs an inner heuristic, a heuristic local search, to construct an optimized sequence of boxes, which is then used to determine their placement within the container. To evaluate the proposed algorithm, we benchmarked its performance against standard test cases from the literature. The experimental results demonstrate its effectiveness and validity.

@article{Ayid2026,
  title     = {Enhanced Harris Hawks Optimization Algorithm with Integrated Tabu Search for 3D Bin Packing Problems: Application in Logistics Industries},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {9},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Yasser M. Ayid and Mohamed Meselhy Eltoukhy and Alaa Mokhtar and Rabie Mosaad},
  doi       = {10.14569/IJACSA.2026.0170988},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170988}
}