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 …
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 …
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 …
A survey on pareto front learning for multi-objective optimization
S Kang, K Li, R Wang - Journal of Membrane Computing, 2024 - Springer
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 …
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 …
A Mutation Lion Swarm Optimization Algorithm Based on Proportional Strategy
Z Zhao, M Jiang, D Yuan, K Jiang, F Wang… - Proceedings of the 2024 …, 2024 - dl.acm.org
During the iterative process, the probability of selection is directly linked to the fitness
magnitude, as each iteration of swarm intelligence optimization algorithms progressively …
magnitude, as each iteration of swarm intelligence optimization algorithms progressively …