.png){kind=icon}
.png){kind=icon}
Return
Metaheuristics for the Minimum Gap Graph Partitioning Problem
.png){kind=icon}
OA
AI
DOI：10.1016/j.cor.2021.105301.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The Minimum Gap Graph Partitioning Problem (MGGPP) consists in partitioning a vertex-weighted undirected graph into a given number of connected subgraphs with the minimum difference between the largest and the smallest weight in each subgraph. We propose a two-level Tabu Search algorithm and an Adaptive Large Neighborhood Search algorithm to solve the MGGPP in reasonable time on instances with up to about 23000 vertices. The quality of the heuristic solutions is assessed comparing them with the solutions of a polynomially solvable combinatorial relaxation.
Keywords:
Graph partitioning
Tabu Search
Adaptive Large Neighborhood Search
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
4.3
Papers:
6.5K
Citations:
1.8W