An Improved Hybrid Genetic Algorithm for the Quadratic Assignment Problem

Abstract

The quadratic assignment problem (QAP) is a well-known optimization problem that has many applications in various engineering areas. Due to its NP-hard nature, rather than the exact methods, heuristic and metaheuristic approaches are commonly adapted. In this study, we propose an improved hybrid genetic algorithm which mainly combines a greedy heuristic, and a simulated annealing algorithm with the classical genetic algorithm. We test our algorithm on the well-known benchmark for the QAP and compare the results with four different algorithms: a greedy algorithm, simulated annealing algorithm (SA), demon algorithm (DA), and a classical genetic algorithm (GA). The results of the experiments validate that our hybridization significantly improves the performance of the algorithms comparing to their standalone executions.