A Parallel Ant Colony Optimization Algorithm Based on Crossover Operation

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 using well-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