Reinforcement learning for combinatorial optimization: A survey
Many traditional algorithms for solving combinatorial optimization problems involve using
hand-crafted heuristics that sequentially construct a solution. Such heuristics are designed …
hand-crafted heuristics that sequentially construct a solution. Such heuristics are designed …
A review on the studies employing artificial bee colony algorithm to solve combinatorial optimization problems
The ABC algorithm is one of the popular optimization algorithms and has been used
successfully in solving many real-world problems. Numeric, binary, integer, mixed integer …
successfully in solving many real-world problems. Numeric, binary, integer, mixed integer …
Difusco: Graph-based diffusion solvers for combinatorial optimization
Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …
promising results in solving various NP-complete (NPC) problems without relying on hand …
Combinatorial optimization and reasoning with graph neural networks
Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …
science. Until recently, its methods have focused on solving problem instances in isolation …
Let the flows tell: Solving graph combinatorial problems with gflownets
Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact
algorithms, making them a tempting domain to apply machine learning methods. The highly …
algorithms, making them a tempting domain to apply machine learning methods. The highly …
Dimes: A differentiable meta solver for combinatorial optimization problems
Recently, deep reinforcement learning (DRL) models have shown promising results in
solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers …
solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers …
Deep reinforcement learning for transportation network combinatorial optimization: A survey
Q Wang, C Tang - Knowledge-Based Systems, 2021 - Elsevier
Traveling salesman and vehicle routing problems with their variants, as classic
combinatorial optimization problems, have attracted considerable attention for decades of …
combinatorial optimization problems, have attracted considerable attention for decades of …
Sym-nco: Leveraging symmetricity for neural combinatorial optimization
Deep reinforcement learning (DRL)-based combinatorial optimization (CO) methods (ie,
DRL-NCO) have shown significant merit over the conventional CO solvers as DRL-NCO is …
DRL-NCO) have shown significant merit over the conventional CO solvers as DRL-NCO is …
Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities
With advances in technologies, data science techniques, and computing equipment, there
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …
Learning to search feasible and infeasible regions of routing problems with flexible neural k-opt
In this paper, we present Neural k-Opt (NeuOpt), a novel learning-to-search (L2S) solver for
routing problems. It learns to perform flexible k-opt exchanges based on a tailored action …
routing problems. It learns to perform flexible k-opt exchanges based on a tailored action …