Ant colonies for the quadratic assignment problem. This paper presents HAS-QAP, a hybrid ant colony system coupled with a local search, applied to the quadratic assignment problem. HAS-QAP uses pheromone trail information to perform modifications on QAP solutions, unlike more traditional ant systems that use pheromone trail information to construct complete solutions. HAS-QAP is analysed and compared with some of the best heuristics available for the QAP: two versions of tabu search, namely, robust and reactive tabu search, hybrid genetic algorithm, and a simulated annealing method. Experimental results show that HAS-QAP and the hybrid genetic algorithm perform best on real world, irregular and structured problems due to their ability to find the structure of good solutions, while HAS-QAP performance is less competitive on random, regular and unstructured problems.

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  1. Bir-Jmel, Ahmed; Douiri, Sidi Mohamed; Elbernoussi, Souad: Gene selection via a new hybrid ant colony optimization algorithm for cancer classification in high-dimensional data (2019)
  2. Kuo, R. J.; Wibowo, B. S.; Zulvia, F. E.: Application of a fuzzy ant colony system to solve the dynamic vehicle routing problem with uncertain service time (2016)
  3. Hussin, Mohamed Saifullah; Stützle, Thomas: Tabu search vs. simulated annealing as a function of the size of quadratic assignment problem instances (2014)
  4. Nguyen, Thanh-Ha; Wright, Mike: Variable neighborhood search for the workload balancing problem in service enterprises (2014)
  5. Czapiński, Michał: An effective parallel multistart tabu search for quadratic assignment problem on CUDA platform (2013) ioport
  6. Misevicius, Alfonsas: An implementation of the iterated tabu search algorithm for the quadratic assignment problem (2012)
  7. Sharma, Vikas K.; Agarwal, Manju; Sen, Kanwar: Reliability evaluation and optimal design in heterogeneous multi-state series-parallel systems (2011) ioport
  8. Agarwal, Manju; Sharma, Vikas K.: Ant colony approach to constrained redundancy optimization in binary systems (2010)
  9. Zhang, Huizhen; Beltran-Royo, Cesar; Constantino, Miguel: Effective formulation reductions for the quadratic assignment problem (2010)
  10. Diab, Nadim; Smaili, Ahmad: Optimum exact/approximate point synthesis of planar mechanisms (2008)
  11. Donati, Alberto V.; Montemanni, Roberto; Casagrande, Norman; Rizzoli, Andrea E.; Gambardella, Luca M.: Time dependent vehicle routing problem with a multi ant colony system (2008)
  12. Drezner, Zvi: Extensive experiments with hybrid genetic algorithms for the solution of the quadratic assignment problem (2008)
  13. Kong, Min; Tian, Peng; Kao, Yucheng: A new ant colony optimization algorithm for the multidimensional Knapsack problem (2008)
  14. Lim, Kwee Kim; Ong, Yew-Soon; Lim, Meng Hiot; Chen, Xianshun; Agarwal, Amit: Hybrid ant colony algorithms for path planning in sparse graphs (2008) ioport
  15. Randall, Marcus: Solution approaches for the capacitated single allocation hub location problem using ant colony optimisation (2008)
  16. Solnon, Christine: Combining two pheromone structures for solving the car sequencing problem with ant colony optimization (2008)
  17. Cheng, Chi-Bin; Mao, Chun-Pin: A modified ant colony system for solving the travelling salesman problem with time windows (2007)
  18. Liao, Ching-Jong; Juan, Hsiao-Chien: An ant colony optimization for single-machine tardiness scheduling with sequence-dependent setups (2007)
  19. Drezner, Zvi; Marcoulides, George A.: Mapping the convergence of genetic algorithms (2006)
  20. Ghoseiri, Keivan; Morshedsolouk, Fahimeh: ACS-TS: Train scheduling using ant colony system (2006)

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