Authors:
Jiayang Chen
1
;
Zhize Wu
1
;
Sarah Thomson
2
and
Thomas Weise
1
Affiliations:
1
Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Jinxiu Dadao 99, Hefei, 230601, Anhui, China
;
2
School of Computing, Engineering & the Built Environment, Edinburgh Napier University, 10 Colinton Road, Edinburgh, EH10 5DT, U.K.
Keyword(s):
Quadratic Assignment Problem, Frequency Fitness Assignment, Randomized Local Search.
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.