On the impact of initialisation strategies on maximum flow algorithm performance
From MaRDI portal
Publication:6551097
DOI10.1016/j.cor.2023.106492MaRDI QIDQ6551097
Kate A. Smith-Miles, Mario Andrés Muñoz, Hossein Alipour
Publication date: 6 June 2024
Published in: Computers \& Operations Research (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Towards objective measures of algorithm performance across instance space
- On the comparison of initialisation strategies in differential evolution for large scale optimisation
- Computational investigations of maximum flow algorithms
- On implementing the push-relabel method for the maximum flow problem
- Instance spaces for machine learning classification
- Testing heuristics: We have it all wrong
- Enhanced instance space analysis for the maximum flow problem
- Maximal Flow Through a Network
- The Pseudoflow Algorithm: A New Algorithm for the Maximum-Flow Problem
- Faster and More Dynamic Maximum Flow by Incremental Breadth-First Search
- The Partial Augment–Relabel Algorithm for the Maximum Flow Problem
- A new approach to the maximum-flow problem
- Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems
- A Computational Study of the Pseudoflow and Push-Relabel Algorithms for the Maximum Flow Problem
- Simplifications and speedups of the pseudoflow algorithm
- Max flows in O(nm) time, or better
This page was built for publication: On the impact of initialisation strategies on maximum flow algorithm performance