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. © 2022 Elsevier B.V. All rights reserved.