Pareto Set Learning for Neural Multi-objective Combinatorial Optimization [PDF]
International Conference on Learning Representations, 2022 Multiobjective combinatorial optimization (MOCO) problems can be found in many real-world applications. However, exactly solving these problems would be very challenging, particularly when they are NP-hard.
Xi Lin, Zhiyuan Yang, Qingfu Zhang
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Simulation-guided Beam Search for Neural Combinatorial Optimization [PDF]
Neural Information Processing Systems, 2022 Neural approaches for combinatorial optimization (CO) equip a learning mechanism to discover powerful heuristics for solving complex real-world problems.
Jinho Choo +6 more
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Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon [PDF]
European Journal of Operational Research, 2018 This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems.
Yoshua Bengio +2 more
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On the emerging potential of quantum annealing hardware for combinatorial optimization [PDF]
Journal of Heuristics, 2022 Over the past decade, the usefulness of quantum annealing hardware for combinatorial optimization has been the subject of much debate. Thus far, experimental benchmarking studies have indicated that quantum annealing hardware does not provide an ...
Byron Tasseff +6 more
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Performance Comparison of Typical Binary-Integer Encodings in an Ising Machine
IEEE Access, 2021 The differences in performance among binary-integer encodings in an Ising machine, which can solve combinatorial optimization problems, are investigated.
Kensuke Tamura +4 more
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A Self-Adaptive Heuristic Algorithm for Combinatorial Optimization Problems [PDF]
International Journal of Computational Intelligence Systems, 2014 This paper introduces a new self-tuning mechanism to the local search heuristic for solving of combinatorial optimization problems. Parameter tuning of heuristics makes them difficult to apply, as parameter tuning itself is an optimization problem.
Cigdem Alabas-Uslu, Berna Dengiz
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Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing
IEEE Access, 2022 The metaheuristic algorithm is a very important area of research that continuously improves in solving optimization problems. Nature-inspired is one of the metaheuristic algorithm classifications that has grown in popularity among researchers over the ...
Aminu Aminu Muazu +2 more
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Fermionic quantum approximate optimization algorithm
Physical Review Research, 2023 Quantum computers are expected to accelerate solving combinatorial optimization problems, including algorithms such as Grover adaptive search and quantum approximate optimization algorithm (QAOA). However, many combinatorial optimization problems involve
Takuya Yoshioka +3 more
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Filtering variational quantum algorithms for combinatorial optimization [PDF]
Quantum Science and Technology, 2021 Current gate-based quantum computers have the potential to provide a computational advantage if algorithms use quantum hardware efficiently. To make combinatorial optimization more efficient, we introduce the filtering variational quantum eigensolver ...
D. Amaro +5 more
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Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers [PDF]
IEEE Transactions on Quantum Engineering, 2021 Combinatorial optimization on near-term quantum devices is a promising path to demonstrating quantum advantage. However, the capabilities of these devices are constrained by high noise or error rates.
Xiaoyuan Liu +5 more
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