Results 21 to 30 of about 8,140,957 (328)
Review of Reinforcement Learning for Combinatorial Optimization Problem [PDF]
Jisuanji kexue yu tansuo, 2022 The solution methods for combinatorial optimization problem (COP) have permeated to the fields of artificial intelligence, operations research, etc. With the scale of data increasing and the speed of problem updating being faster, the traditional method ...
WANG Yang, CHEN Zhibin, WU Zhaorui, GAO Yuan
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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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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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On the Landscape of Combinatorial Optimization Problems [PDF]
IEEE Transactions on Evolutionary Computation, 2014 This paper carries out a comparison of the fitness landscape for four classic optimization problems: Max-Sat, graph-coloring, traveling salesman, and quadratic assignment. We have focused on two types of properties, local average properties of the landscape, and properties of the local optima.
Tayarani Najaran, Mohammad +1 more
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A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization [PDF]
ACM Transactions on Knowledge Discovery from Data, 2022 Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems.
Yan-Di Li +4 more
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Combinatorial optimization problems with balanced regret
Discrete Applied Mathematics, 2023 For decision making under uncertainty, min-max regret has been established as a popular methodology to find robust solutions. In this approach, we compare the performance of our solution against the best possible performance had we known the true scenario in advance.
Marc Goerigk, Michael Hartisch
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Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems [PDF]
IEEE Transactions on Cybernetics, 2021 This article introduces a new deep learning approach to approximately solve the covering salesman problem (CSP). In this approach, given the city locations of a CSP as input, a deep neural network model is designed to directly output the solution.
Kaiwen Li +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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An Approach to Aid Decision-Making by Solving Complex Optimization Problems Using SQL Queries
Applied Sciences, 2022 In combinatorial optimization, the more complex a problem is, the more challenging it becomes, usually causing most research to focus on creating solvers for larger cases.
Jose Torres-Jimenez +3 more
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Analysis of cutting stock problem metaheuristic algorithms
Lietuvos Matematikos Rinkinys, 2021 The analysis of cutting stock problem and heuristic and metaheuristic algorithms for solving it are presented in this paper.
Jonas Pokštas, Narimantas Listopadskis
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