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Quantum annealing-driven topology optimization design method for truss structures |
Received:March 27, 2025 Revised:May 08, 2025 |
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DOI:10.7511/jslx20250326002 |
KeyWord:truss structures topology optimal design quantum annealing algorithm quantum computing quadratic unconstrained binary optimization problem |
Author | Institution |
王炎 |
大连理工大学 工程力学系, 工业装备结构分析优化与CAE软件全国重点实验室, 大连 |
杨迪雄 |
大连理工大学 工程力学系, 工业装备结构分析优化与CAE软件全国重点实验室, 大连 |
雷振增 |
大连理工大学 工程力学系, 工业装备结构分析优化与CAE软件全国重点实验室, 大连 |
陈国海 |
大连理工大学 工程力学系, 工业装备结构分析优化与CAE软件全国重点实验室, 大连 |
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Abstract: |
To address the limitations of computational inefficiency and ease to fall into local optima in traditional topology optimization methods for a truss structures,this paper proposes a quantum annealing-driven stiffness optimization design method based on the idea of a truss ground structure.Firstly,the elastic strain energy of the truss structure is selected as the objective function,while volume constraints are formulated as penalty terms embedded into the Hamiltonian of the truss system,thereby forming the problem of an unconstrained optimization.Subsequently,by constructing the exponential mapping relationship between the increment of the cross-sectional area of the bars and the encoded qubit,introducing auxiliary qubits and driving them by a quantum annealing algorithm,the adjustment of the positive and negative directions of the increment is realized.The volume-constrained truss stiffness optimization problem is transformed into a quadratic unconstrained binary optimization problem which is solvable by a quantum annealing machine.The hybrid optimization framework incorporating classical-quantum computing is developed,where classical finite element analysis is executed on a conventional computer and the optimization task is implemented on the special quantum annealer.Two examples of truss topology optimization indicate the stable convergence of the proposed algorithm of this paper.This work demonstrates the feasibility and application potential of the quantum annealing algorithm in structural optimization,offering an innovative paradigm for engineering design optimization that integrates classical computing with quantum algorithms. |
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