Rl4co: an extensive reinforcement learning for combinatorial optimization benchmark
We introduce RL4CO, an extensive reinforcement learning (RL) for combinatorial
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives
Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …
A survey on Pareto front learning for multi-objective optimization
Multi-objective optimization (MOO) is challenging since it needs to deal with multiple
conflicting objectives. Multi-objective evolutionary algorithms (MOEAs) are the mainstream …
conflicting objectives. Multi-objective evolutionary algorithms (MOEAs) are the mainstream …
Efficient neural collaborative search for pickup and delivery problems
In this paper, we introduce Neural Collaborative Search (NCS), a novel learning-based
framework for efficiently solving pickup and delivery problems (PDPs). NCS pioneers the …
framework for efficiently solving pickup and delivery problems (PDPs). NCS pioneers the …
MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts
Learning to solve vehicle routing problems (VRPs) has garnered much attention. However,
most neural solvers are only structured and trained independently on a specific problem …
most neural solvers are only structured and trained independently on a specific problem …
Dealing With Structure Constraints in Evolutionary Pareto Set Learning
In the past few decades, many multiobjective evolutionary optimization algorithms (MOEAs)
have been proposed to find a finite set of approximate Pareto solutions for a given problem …
have been proposed to find a finite set of approximate Pareto solutions for a given problem …
Learning to Solve Quadratic Unconstrained Binary Optimization in a Classification Way
The quadratic unconstrained binary optimization (QUBO) is a well-known NP-hard problem
that takes an $ n\times n $ matrix $ Q $ as input and decides an $ n $-dimensional 0-1 vector …
that takes an $ n\times n $ matrix $ Q $ as input and decides an $ n $-dimensional 0-1 vector …
Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond
Multi-objective optimization (MOO) in deep learning aims to simultaneously optimize
multiple conflicting objectives, a challenge frequently encountered in areas like multi-task …
multiple conflicting objectives, a challenge frequently encountered in areas like multi-task …
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems
The profiled vehicle routing problem (PVRP) is a generalization of the heterogeneous
capacitated vehicle routing problem (HCVRP) in which the objective is to optimize the routes …
capacitated vehicle routing problem (HCVRP) in which the objective is to optimize the routes …
Offline Multi-Objective Optimization
Offline optimization aims to maximize a black-box objective function with a static dataset and
has wide applications. In addition to the objective function being black-box and expensive to …
has wide applications. In addition to the objective function being black-box and expensive to …