Automation, Control and Intelligent Systems
Volume 1, Issue 3, June 2013, Pages: 53-58
Received: Jun. 12, 2013;
Published: Jun. 30, 2013
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Matej Ciba, Institute of Control and Industrial Informatics, Bratislava, Slovakia
Ivan Sekaj, Institute of Control and Industrial Informatics, Bratislava, Slovakia
This paper attempts to overcome stagnation problem of Ant Colony Optimization (ACO) algorithms. Stagnation is undesirable state which occurs at a later phases of the search process. Excessive pheromone values attract more ants and make further exploration hardly possible. This problem has been addressed by Genetic operations (GO) incorporated into ACO framework. Crossover and mutation operations have been adapted for use with ant generated strings which still have to provide feasible solutions. Genetic operations decrease selection pressure and increase probability of finding the global optimum. Extensive simulation tests were made in order to determine influence of genetic operation on algorithm performance.
Ant Colony Optimization with Genetic Operations, Automation, Control and Intelligent Systems.
Vol. 1, No. 3,
2013, pp. 53-58.
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