A connectionist approach to the quadratic assignment problem
Computers and Operations Research - Special issue on neural networks and operations research
Extensions of a tabu search adaptation to the quadratic assignment problem
Computers and Operations Research - Special issue: heuristic, genetic and tabu search
A genetic approach to the quadratic assignment problem
Computers and Operations Research - Special issue on genetic algorithms
Computational Optimization and Applications
A greedy genetic algorithm for the quadratic assignment problem
Computers and Operations Research
Future Generation Computer Systems
QAPLIB – A Quadratic Assignment ProblemLibrary
Journal of Global Optimization
Extensive Testing of a Hybrid Genetic Algorithm for Solving Quadratic Assignment Problems
Computational Optimization and Applications
The Ant System Applied to the Quadratic Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
A Parallel Genetic Heuristic for the Quadratic Assignment Problem
Proceedings of the 3rd International Conference on Genetic Algorithms
GAACO: A GA + ACO Hybrid for Faster and Better Search Capability
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
A New Genetic Algorithm for the Quadratic Assignment Problem
INFORMS Journal on Computing
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new minimum pheromone threshold strategy (MPTS) for max-min ant system
Applied Soft Computing
The design and implementation of a competency-based intelligent mobile learning system
Expert Systems with Applications: An International Journal
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In recent decades, many meta-heuristics, including genetic algorithm (GA), ant colony optimization (ACO) and various local search (LS) procedures have been developed for solving a variety of NP-hard combinatorial optimization problems. Depending on the complexity of the optimization problem, a meta-heuristic method that may have proven to be successful in the past might not work as well. Hence it is becoming a common practice to hybridize meta-heuristics and local heuristics with the aim of improving the overall performance. In this paper, we propose a novel adaptive GA-ACO-LS hybrid algorithm for solving quadratic assignment problem (QAP). Empirical study on a diverse set of QAP benchmark problems shows that the proposed adaptive GA-ACO-LS converges to good solutions efficiently. The results obtained were compared to the recent state-of-the-art algorithm for QAP, and our algorithm showed obvious improvement.