ACO algorithms for the quadratic assignment problem
New ideas in optimization
A greedy genetic algorithm for the quadratic assignment problem
Computers and Operations Research
QAPLIB – A Quadratic Assignment ProblemLibrary
Journal of Global Optimization
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
A New Genetic Algorithm for the Quadratic Assignment Problem
INFORMS Journal on Computing
A tabu search algorithm for the quadratic assignment problem
Computational Optimization and Applications
Sequential and Parallel Path-Relinking Algorithms for the Quadratic Assignment Problem
IEEE Intelligent Systems
A Hybrid Metaheuristic for the Quadratic Assignment Problem
Computational Optimization and Applications
A new iterated fast local search heuristic for solving QAP formulation in facility layout design
Robotics and Computer-Integrated Manufacturing
Multistart tabu search and diversification strategies for the quadratic assignment problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hierarchical Iterated Local Search for the Quadratic Assignment Problem
HM '09 Proceedings of the 6th International Workshop on Hybrid Metaheuristics
Consultant-guided search algorithms for the quadratic assignment problem
HM'10 Proceedings of the 7th international conference on Hybrid metaheuristics
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
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This paper introduces a new path relinking algorithm for the well-known quadratic assignment problem QAP in combinatorial optimization. The QAP has attracted considerable attention in research because of its complexity and its applicability to many domains. The algorithm presented in this study employs path relinking as a solution combination method incorporating a multistart tabu search algorithm as an improvement method. The resulting algorithm has interesting similarities and contrasts with particle swarm optimization methods. Computational testing indicates that this algorithm produces results that rival the best QAP algorithms. The authors additionally conduct an analysis disclosing how different strategies prove more or less effective depending on the landscapes of the problems to which they are applied. This analysis lays a foundation for developing more effective future QAP algorithms, both for methods based on path relinking and tabu search, and for hybrids of such methods with related processes found in particle swarm optimization.