A parallel ant colony optimization algorithm based on crossover operation

Kalınlı, Adem
Sarıkoç, Fatih
In this work, we introduce a new parallel ant colony optimization algorithm based on an ant metaphor and the crossover operator from genetic algorithms.The performance of the proposed model is evaluated usingwell-known numerical test problems and then it is applied to train recurrent neural networks to identify linear and nonlinear dynamic plants. The simulation results are compared with results using other algorithms.


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Citation Formats
A. Kalınlı and F. Sarıkoç, A parallel ant colony optimization algorithm based on crossover operation. 2018, p. 110.