Hierarchical hybrid Ising simulation solution method for multi-agent task allocation and collaborative path planning

Yixin Lu; Jijun Feng

doi:10.54254/2977-3903/2026.32648

Article Information

Received: 3 April 2026

Published: 10 April 2026

DOI: https://doi.org/10.54254/2977-3903/2026.32648

Article Type: Research Article

Cite this article

Lu,Y.;Feng,J. (2026). Hierarchical hybrid Ising simulation solution method for multi-agent task allocation and collaborative path planning. Advances in Engineering Innovation,17(4),214-220.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About this Journal

Journal: Advances in Engineering Innovation

Volume: Vol.17, Issue 4

ISSN: 2977-3903 (Print) / 2977-3911 (Online)

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[1]B. P. Gerkey, M. J. Matarić (2004). A Formal Analysis and Taxonomy of Task Allocation in Multi-Robot Systems.The International Journal of Robotics Research,23(9), 939–954. DOI: 10.1177/0278364904045564.

[2]R. Stern, N. Sturtevant, A. Felner, S. Koenig, H. Ma, T. Walker, J. Li, D. Atzmon, L. Cohen, T. K. S. Kumar, E. Boyarski, R. Barták (2019). Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks.Proceedings of SoCS. DOI: 10.48550/arXiv.1906.08291.

[3]G. Sharon, R. Stern, A. Felner, N. R. Sturtevant (2015). Conflict-based Search for Optimal Multi-Agent Pathfinding.Artificial Intelligence,219, 40–66. DOI: 10.1016/j.artint.2014.11.006.

[4]S. Kirkpatrick, C. D. Gelatt Jr., M. P. Vecchi (1983). Optimization by Simulated Annealing.Science,220(4598), 671–680. DOI: 10.1126/science.220.4598.671.

[5]A. Lucas (2014). Ising Formulations of Many NP Problems.Frontiers in Physics,2, 5. DOI: 10.3389/fphy.2014.00005.

[6]F. Glover, G. Kochenberger, Y. Du (2018). A Tutorial on Formulating and Using QUBO Models. arXiv: 1811.11538. DOI: 10.48550/arXiv.1811.11538.

[7]E. S. Tiunov, A. E. Ulanov, A. I. Lvovsky (2019). Annealing by Simulating the Coherent Ising Machine.Optics Express,27(7), 10288–10295. DOI: 10.1364/OE.27.010288.

[8]H. Goto, K. Tatsumura, A. R. Dixon (2019). Combinatorial Optimization by Simulating Adiabatic Bifurcations in Nonlinear Hamiltonian Systems.Science Advances,5(4), eaav2372. DOI: 10.1126/sciadv.aav2372.

[9]T. Honjo, T. Sonobe, K. Inaba, et al. (2021). 100000-spin coherent Ising machine.Science Advances,7(40), eabh0952. DOI: 10.1126/sciadv.abh0952.

[10]C. Roques-Carmes, Y. Shen, C. Zanoci, et al. (2020). Heuristic Recurrent Algorithms for Photonic Ising Machines.Nature Communications,11, 249. DOI: 10.1038/s41467-019-14096-z.

[11]Y. Gao, G. Chen, L. Qi, W. Fu, Z. Yuan, A. J. Danner (2024). Photonic Ising Machines for Combinatorial Optimization Problems.Applied Physics Reviews,11, 041307. DOI: 10.1063/5.0216656.

[12]A. Yamamura, H. Mabuchi, S. Ganguli (2024). Geometric Landscape Annealing as an Optimization Principle Underlying the Coherent Ising Machine.Physical Review X,14(3), 031054. DOI: 10.1103/PhysRevX.14.031054.

[13]C. Bybee, G. M. Rosiles, D. A. Sartor, et al. (2023). Efficient Optimization with Higher-Order Ising Machines.Nature Communications,14, 6399. DOI: 10.1038/s41467-023-41214-9.

[14]D. Veraldi, D. Pierangeli, S. Gentilini, et al. (2025). Fully Programmable Spatial Photonic Ising Machine by Focal Plane Division.Physical Review Letters,134(6), 063802. DOI: 10.1103/PhysRevLett.134.063802.

[15]Z. Hu, et al. (2026). Programmable Optoelectronic Ising Machine for Optimization of Real-World Problems.Light: Science & Applications. DOI: 10.1038/s41377-025-02100-9.