Perturbation-Based Thresholding Search for Packing Equal Circles and Spheres

This paper presents an effective perturbation-based thresholding search for two popular and challenging packing problems with minimal containers: packing N identical circles in a square and packing N identical spheres in a cube. Following the penalty function approach, we handle these constrained optimization problems by solving a series of unconstrained optimization subproblems with fixed containers. The proposed algorithm relies on a two-phase search strategy that combines a thresholding search method reinforced by two general-purpose perturbation operators and a container adjustment method. The performance of the algorithm is assessed relative to a large number of benchmark instances widely studied in the literature. Computational results show a high performance of the algorithm on both problems compared with the state-of-the-art results. For circle packing, the algorithm improves 156 best-known results (new upper bounds) in the range of 2 < N < 400 and matches 242 other best-known results. For sphere packing, the algorithm improves 66 best-known results in the range of 2 < N < 200, whereas matching the best-known results for 124 other instances. Experimental analyses are conducted to shed light on the main search ingredients of the proposed algorithm consisting of the two-phase search strategy, the mixed perturbation and the parameters.

Automated summary

This paper presents a perturbation-based thresholding search algorithm for packing N identical circles in a square and N identical spheres in a cube. The algorithm combines a two-phase search strategy, perturbation operators, and container adjustment to achieve high performance. Experimental results show significant improvements compared with state-of-the-art results for both circle and sphere packing problems.