Research Article
[Retracted] Gender-Based Deep Learning Firefly Optimization Method for Test Data Generation
Algorithm 1
The pseudo code of Gen-DLFA.
| (1) | Initialize the parameters of algorithm; | | (2) | Initialize firefly population randomly as in (1); | | (3) | Calculate brightness of each firefly according to fitness function; | | (4) | while (iterator < maxGen){ | | (5) | for the male firefly : | | (6) | for to | | (7) | Select a female randomly from female subgroup | | (8) | if is brighter than | | (9) | move to as in (8); | | (10) | update the position of | | (11) | End if; | | (12) | End for; | | (13) | construct general center firefly of male subgroup as in (9); | | (14) | conduct deep learning of general center firefly as in (10); | | (15) | for the female firefly : | | (16) | for to | | (17) | if general center firefly is brighter than | | (18) | move to general center according as in (11); | | (19) | update the position of ; | | (20) | else | | (21) | conduct cauchy mutation of as in (12); | | (22) | update the position of ; | | (23) | End if; | | (24) | End for; | | (25) | rank the firefly population and find the best solution ; | | (26) | for to k | | (27) | implement chaotic search near to get | | (28) | if ( is brighter than ) | | (29) | ; | | (30) | End if; | | (31) | End for; | | (32) | output the ; | | (33) | iterator++; | | (34) | End while; |
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