Results 1 to 10 of about 14,288 (113)
Mathematics, 2021 Combinatorial optimization problems are often considered NP-hard problems in the field of decision science and the industrial revolution. As a successful transformation to tackle complex dimensional problems, metaheuristic algorithms have been ...
Md Ashikur Rahman +5 more
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Ferroelectric compute-in-memory annealer for combinatorial optimization problems. [PDF]
Nat CommunComputationally hard combinatorial optimization problems (COPs) are ubiquitous in many applications. Various digital annealers, dynamical Ising machines, and quantum/photonic systems have been developed for solving COPs, but they still suffer from the ...
Yin X +13 more
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Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development
Applied Sciences, 2021 In the past few decades, metaheuristics have demonstrated their suitability in addressing complex problems over different domains. This success drives the scientific community towards the definition of new and better-performing heuristics and results in ...
Fernando Peres, Mauro Castelli
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Adaptive Shannon Entropy-Driven Chaotic Grey Wolf Optimization (ASE-CGWO) for Combinatorial Optimization Problems. [PDF]
Biomimetics (Basel)Combinatorial optimization problems include many NP-hard problems such as assignment, scheduling, and routing, for which conventional approaches to solving become computationally infeasible for large instances, thereby necessitating the use of ...
Kalathodi S +5 more
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A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems
IEEE Access, 2020 Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is to determine the best approach for solving a problem at its current state. This way, every time we make a move it gets us closer to a solution.
Melissa Sanchez +5 more
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Operations Research Perspectives, 2015 Many combinatorial optimization problems (COPs) encountered in real-world logistics, transportation, production, healthcare, financial, telecommunication, and computing applications are NP-hard in nature. These real-life COPs are frequently characterized
Angel A. Juan +4 more
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Hysteresis in Combinatorial Optimization Problems
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 Hysteresis is a physical phenomenon reflected in macroscopic observables of materials that are subjected to external perturbations. For example, magnetic hysteresis is observed in ferromagnetic metals such as iron, nickel and cobalt in the presence of a ...
Yuling Guan +4 more
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A Quantum-Inspired Tensor Network Algorithm for Constrained Combinatorial Optimization Problems
Frontiers in Physics, 2022 Combinatorial optimization is of general interest for both theoretical study and real-world applications. Fast-developing quantum algorithms provide a different perspective on solving combinatorial optimization problems.
Tianyi Hao +5 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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Dynamical System-Based Computational Models for Solving Combinatorial Optimization on Hypergraphs
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 2023 The intrinsic energy minimization in dynamical systems offers a valuable tool for minimizing the objective functions of computationally challenging problems in combinatorial optimization. However, most prior works have focused on mapping such dynamics to
Mohammad Khairul Bashar +3 more
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