English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We present NeuroLKH, a novel algorithm that combines deep learning with thestrong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving TravelingSalesman Problem. Specifically, we train a Sparse Graph Network (SGN) withsupervised learning for edge scores and unsupervised learning for nodepenalties, both of which are critical for improving the performance of LKH.Based on the output of SGN, NeuroLKH creates the edge candidate set andtransforms edge distances to guide the searching process of LKH. Extensiveexperiments firmly demonstrate that, by training one model on a wide range ofproblem sizes, NeuroLKH significantly outperforms LKH and generalizes well tomuch larger sizes. Also, we show that NeuroLKH can be applied to other routingproblems such as Capacitated Vehicle Routing Problem (CVRP), Pickup andDelivery Problem (PDP), and CVRP with Time Windows (CVRPTW).

NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem