10 papers with code ·

Despite the computational expense, without much engineering and heuristic designing, Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes.

Achieving fusion of deep learning with combinatorial algorithms promises transformative changes to artificial intelligence.

Furthermore, to approximate solutions to constrained combinatorial optimization problems such as the TSP with time windows, we train hierarchical GPNs (HGPNs) using RL, which learns a hierarchical policy to find an optimal city permutation under constraints.

SOTA for Traveling Salesman Problem on TSPLIB

COMBINATORIAL OPTIMIZATION GRAPH EMBEDDING HIERARCHICAL REINFORCEMENT LEARNING TRAVELING SALESMAN PROBLEM

We evaluate greedy, 2-opt, and genetic algorithms.

We propose a new neural network architecture and use it for the task of statement-by-statement alignment of source code and its compiled object code.

In this paper, we propose a method to solve a bi-objective variant of the well-studied Traveling Thief Problem (TTP).

The results show that our MMAS implementation is competitive with state-of-the-art GPU-based and multi-core CPU-based parallel ACO implementations: in fact, the times obtained for the Nvidia V100 Volta GPU were up to 7. 18x and 21. 79x smaller, respectively.

This paper addresses the Traveling Salesman Problem with Drone (TSP-D), in which a truck and drone are used to deliver parcels to customers.

The first algorithm (TSP-LS) was adapted from the approach proposed by Murray and Chu (2015), in which an optimal TSP solution is converted to a feasible TSP-D solution by local searches.

Genetic algorithm includes some parameters that should be adjusting so that the algorithm can provide positive results.