Research Article
Air Target Threat Assessment Based on Improved Moth Flame Optimization-Gray Neural Network Model
Algorithm 1
Improvement moth flame optimization algorithm.
| (1) | Initialize solution population using tent chaos map | | (2) | iteration = 1 | | (3) | while (iteration ≤ Max_iteration) | | (4) | OM = FitnessFunction(M) | | (5) | if iteration = = 1 | | (6) | F = sort(M) | | (7) | OF = sort(OM) | | (8) | else | | (9) | F = sort(Mt−1, Mt) | | (10) | OF = sort(Mt-1, Mt) | | (11) | end if | | (12) | for i = 1 : n | | (13) | for j = 1 : d | | (14) | update t | | (15) | calculate D with respect to the corresponding flame | | (16) | update M(i, j) using equation (15) with respect to the corresponding flame | | (17) | end for | | (18) | end for | | (19) | update the position of the current optimal agent using Lévy-flight | | (20) | F_lévy = Lévy(F) | | (21) | OF_lévy = FitnessFunction(F_lévy) | | (22) | using the Metropolis criterion for OF and OF_lévy | | (23) | update the position best flame obtained so far | | (24) | update flame number using equation (16) | | (25) | iteration = iteration + 1 | | (26) | end while |
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