Abstract
This paper presents a variable neighborhood search (VNS) heuristic for solving the heaviest k-subgraph problem. Different versions of the heuristic are examined including 'skewed' VNS and a combination of a constructive heuristic followed by VNS. Extensive computational experiments are performed on a series of large random graphs as well as several instances of the related maximum diversity problem taken from the literature. The results obtained by VNS were consistently the best over a number of other heuristics tested.
Original language | English |
---|---|
Pages (from-to) | 2885-2891 |
Number of pages | 7 |
Journal | Computers and Operations Research |
Volume | 36 |
Issue number | 11 |
DOIs | |
Publication status | Published - 1 Nov 2009 |
Keywords
- Combinatorial optimization
- Heaviest k-subgraph
- Maximum diversity
- Metaheuristics
- Variable neighborhood search
ASJC Scopus subject areas
- Computer Science(all)
- Modelling and Simulation
- Management Science and Operations Research