@inproceedings{547bf167f30044738c5e2434e78598b7,
title = "Incremental local search in ant colony optimization: Why it fails for the quadratic assignment problem",
abstract = "Ant colony optimization algorithms are currently among the best performing algorithms for the quadratic assignment problem. These algorithms contain two main search procedures: solution construction by artificial ants and local search to improve the solutions constructed by the ants. Incremental local search is an approach that consists in re-optimizing partial solutions by a local search algorithm at regular intervals while constructing a complete solution. In this paper, we investigate the impact of adopting incremental local search in ant colony optimization to solve the quadratic assignment problem. Notwithstanding the promising results of incremental local search reported in the literature in a different context, the computational results of our new ACO algorithm are rather negative. We provide an empirical analysis that explains this failure.",
author = "Prasanna Balaprakash and Mauro Birattari and Thomas St{\"u}tzle and Marco Dorigo",
year = "2006",
doi = "10.1007/11839088_14",
language = "English",
isbn = "3540384820",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "156--166",
booktitle = "Ant Colony Optimization and Swarm Intelligence - 5th International Workshop, ANTS 2006, Proceedings",
note = "Ant Colony Optimization and Swarm Intelligence - 5th International Workshop, ANTS 2006, Proceedings ; Conference date: 04-09-2006 Through 07-09-2006",
}