Application of Continuous ACOR to Neural Network Training: Direction of Arrival Problem

Structural Damage Detection Based on Modal Parameters Using Continuous Ant Colony Optimization

“…Again, it may require great amount of computation time for getting the results. To minimize the convergence time and to increase the accuracy there have been a number of works on algorithm refinements and hybridization [35][36][37][38]. However, the performance of ACO R algorithm greatly depends on the parameters of the algorithm, that is, , , and .…”

“…For the present study 10 solutions are added to solution archive per iteration and equal numbers of worst solutions are deleted from the solution archive. In order to avoid assignment of weights close to zero, to the last solutions of the sorted ants, Movahedipour [35] defined a limit C and find corresponding value as…”

Hybrid neural network for classification problem solving

“…The experiments showed that the offered approach allows overcoming BP algorithm shortcomings. In article [16] the hybrid ANN training algorithm is applied to the Direction of Arrival estimation problem solution. The hybrid algorithm is based on the continuous ACO.…”

“…The hybrid of these two algorithms is applied to predict the permeability of the Mansuri Bangestan reservoir located in Ahwaz Iran utilizing available geophysical well log data. The given works [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] suggest different hybrid methods and give their advantages in comparison with the classical ones, their application to different classification problems. The hybrid training methods allow applying the hybrid approach more effectively to solve a wide class of problems avoiding the disadvantages of the classical methods.…”