A Study of Scalarisation Techniques for Multi-objective QUBO Solving
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-24907-5_47
Reference12 articles.
1. Aramon, M., Rosenberg, G., Valiante, E., Miyazawa, T., Tamura, H., & Katzgraber, H. G. (2019). Physics-inspired optimization for quadratic unconstrained problems using a digital Annealer. Frontiers in Physics, 7, 48.
2. Ayodele, M. (2022). Comparing the digital Annealer with classical evolutionary algorithm. arXiv preprint arXiv:2205.13586
3. Ayodele, M. (2022). Penalty weights in QUBO formulations: Permutation problems. In European Conference on Evolutionary Computation in Combinatorial Optimization (Part of EvoStar) (pp. 159–174). Springer.
4. Ayodele, M., Allmendinger, R., López-Ibánez, M., & Parizy, M. (2022). Multi-objective QUBO solver: Bi-objective quadratic assignment problem. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ’22. ACM Press. https://doi.org/10.1145/3512290.3528698
5. Chang, T. J., Meade, N., Beasley, J., & Sharaiha, Y. (2000). Heuristics for cardinality constrained portfolio optimisation. Computers and Operations Research, 27(13), 1271–1302.
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