# A Virtual-Force Based Swarm Algorithm for Balanced Circular Bin Packing Problems

arXiv: https://arxiv.org/abs/2306.01021 · HTML: https://mlsanity.com/paper/2306.01021/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 59 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Online 3D Bin Packing with Constrained Deep Reinforcement Learning](https://mlsanity.com/paper/2006.14978/) (2020)
- [Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method](https://mlsanity.com/paper/1708.05930/) (2017)
- [Hidden-Variables Genetic Algorithm for Variable-Size Design Space Optimal Layout Problems with Application to Aerospace Vehicles](https://mlsanity.com/paper/2212.11011/) (2022)
- [An Efficient Quasi-physical Quasi-human Algorithm for Packing Equal Circles in a Circular Container](https://mlsanity.com/paper/1611.02323/) (2016)
- [Adaptive Simulated Annealing with Greedy Search for the Circle Bin Packing Problem](https://mlsanity.com/paper/2108.03203/) (2021)

## Abstract (arXiv)

Balanced circular bin packing problems consist in positioning a given number of weighted circles in order to minimize the radius of a circular container while satisfying equilibrium constraints. These problems are NP-hard, highly constrained and dimensional. This paper describes a swarm algorithm based on a virtual-force system in order to solve balanced circular bin packing problems. In the proposed approach, a system of forces is applied to each component allowing to take into account the constraints and minimizing the objective function using the fundamental principle of dynamics. The proposed algorithm is experimented and validated on benchmarks of various balanced circular bin packing problems with up to 300 circles. The reported results allow to assess the effectiveness of the proposed approach compared to existing results from the literature.