Frequency Fitness Assignment: Optimization Without Bias for Good Solution Outperforms Randomized Local Search on the Quadratic Assignment Problem

Jiayang Chen, Zhize Wu, Sarah Thomson, Thomas Weise

2024

Abstract

The Quadratic Assignment Problem (QAP) is one of the classical NP-hard tasks from operations research with a history of more than 65 years. It is often approached with heuristic algorithms and over the years, a multitude of such methods has been applied. All of them have in common that they tend to prefer better solutions over worse ones. We approach the QAP with Frequency Fitness Assignment (FFA), an algorithm module that can be plugged into arbitrary iterative heuristics and that removes this bias. One would expect that a heuristic that does not care whether a new solution is better or worse compared to the current one should not perform very well. We plug FFA into a simple randomized local search (RLS) and yield the FRLS, which surprisingly outperforms RLS on the vast majority of the instances of the well-known QAPLIB benchmark set.

Paper Citation

in Harvard Style

Chen J., Wu Z., Thomson S. and Weise T. (2024). Frequency Fitness Assignment: Optimization Without Bias for Good Solution Outperforms Randomized Local Search on the Quadratic Assignment Problem. In Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: ECTA; ISBN 978-989-758-721-4, SciTePress, pages 27-37. DOI: 10.5220/0012888600003837

in Bibtex Style

@conference{ecta24,
author={Jiayang Chen and Zhize Wu and Sarah Thomson and Thomas Weise},
title={Frequency Fitness Assignment: Optimization Without Bias for Good Solution Outperforms Randomized Local Search on the Quadratic Assignment Problem},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: ECTA},
year={2024},
pages={27-37},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012888600003837},
isbn={978-989-758-721-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: ECTA
TI - Frequency Fitness Assignment: Optimization Without Bias for Good Solution Outperforms Randomized Local Search on the Quadratic Assignment Problem
SN - 978-989-758-721-4
AU - Chen J.
AU - Wu Z.
AU - Thomson S.
AU - Weise T.
PY - 2024
SP - 27
EP - 37
DO - 10.5220/0012888600003837
PB - SciTePress