Şeyda Melis TürkkahramanDindar Öz2025-10-062021978166542908510.1109/UBMK52708.2021.9558978https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125878062&doi=10.1109%2FUBMK52708.2021.9558978&partnerID=40&md5=167b060b60666a4a42047d7d4c3fa058https://gcris.yasar.edu.tr/handle/123456789/9037The 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.EnglishGenetic Algorithm, Greedy Algorithm, Heuristics, Hybrid Algorithm, Metaheuristics, Quadratic Assignment Problem, Simulated Annealing Algorithm, Combinatorial Optimization, Genetic Algorithms, Heuristic Methods, Annealing Algorithm, Greedy Algorithms, Heuristic, Hybrid Algorithms, Improved Hybrid Genetic Algorithm, Metaheuristic, Np-hard, Optimization Problems, Quadratic Assignment Problems, Simulated Annealing Algorithm, Simulated AnnealingCombinatorial optimization, Genetic algorithms, Heuristic methods, Annealing algorithm, Greedy algorithms, Heuristic, Hybrid algorithms, Improved hybrid genetic algorithm, Metaheuristic, NP-hard, Optimization problems, Quadratic assignment problems, Simulated annealing algorithm, Simulated annealingConference Object