A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem

TL;DR

This paper compares genetic algorithms, tabu search, and simulated annealing for solving the NP-hard quadratic assignment problem, analyzing their efficiency and solution quality on real-world instances.

Contribution

It provides a comparative analysis of three meta-heuristic algorithms applied to QAP, highlighting their relative strengths and weaknesses.

Findings

Genetic Algorithm yields the best solution quality.

Tabu Search has the fastest runtime.

Simulated annealing offers a balanced trade-off.

Abstract

Quadratic Assignment Problem (QAP) is an NP-hard combinatorial optimization problem, therefore, solving the QAP requires applying one or more of the meta-heuristic algorithms. This paper presents a comparative study between Meta-heuristic algorithms: Genetic Algorithm, Tabu Search, and Simulated annealing for solving a real-life (QAP) and analyze their performance in terms of both runtime efficiency and solution quality. The results show that Genetic Algorithm has a better solution quality while Tabu Search has a faster execution time in comparison with other Meta-heuristic algorithms for solving QAP.