| | Input: DFILE ⟵ Dataset file for the simulation |
| | Output:IDS-Net ⟵ Final Route from VS to VD in VANET CP ← Classification Parameters |
| (1) | Final Record = [] |
| (2) | Count = 1 |
| (3) | For I in range (Normalized Data, Col) |
| (4) | Current Feature Col = Normalized Data (All Row, I) |
| (5) | All Grouped Bee Records = [] |
| (6) | For J in range (5) |
| (7) | Ebee = [Current Feature Col (1), Other five Current Feature Col (Randomly)] |
| (8) | |
| (9) | Define fitness function of G-ABC |
| | (i) All Fit Record = [] |
| | (ii) Fit Status = 0 |
| | (iii) For K in range (Ebee) |
| | (iv) If Ebee (K) > Obee |
| | (v) Fit Status = 1 |
| | (vi) Else |
| | (vii) Fit Status = 0 |
| | (viii) End–If |
| | (b) All Fit Record (K) = Fit Status |
| | (c) End–For |
| (10) | End–For |
| (11) | All Fit = fitness function (Ebee, Obee) |
| (12) | If count of non-zeros in All Fit >1 |
| (13) | Bee Status = 1 |
| (14) | Else |
| (15) | Bee Status = 0 |
| (16) | End–If |
| (17) | All Bee Record (J) = Bee Status |
| (18) | End–For |
| (19) | If count of non-zeros in All Bee Record > Average (All Bee Record) |
| (20) | Final Record (count) = I |
| (21) | Count = Count + 1 |
| (22) | End–If |
| (23) | End–For |
| (24) | Select Data from Normalized Data according to selected index by G-ABC |
| (25) | Selected Normalized Data = Normalized Data(All Row, Final Record) |
| (26) | Create Target for Model Training |
| (27) | Target = []//Create an empty variable to store Target |
| (28) | For I in range (Selected Normalized Data, Row) |
| (29) | If ε 1st Unique Label |
| (30) | Target (1, I) = 1 |
| (31) | Else if ε 2nd Unique Label |
| (32) | Target (2, I) = 2 |
| (33) | Else if ε 3rd Unique Label |
| (34) | Target (3, I) = 3 |
| (35) | Else if ε 4th Unique Label |
| (36) | Target (4, I) = 4 |
| (37) | Else if ε 5th Unique Label |
| (38) | Target (5, I) = 5 |
| (39) | End–If |
| (40) | End–For |
| (41) | Call Deep Neural Network with 10 Hidden Layers |
| (42) | IDS-Net = pattern net (10) |
| (43) | IDS-Net = train (IDS-Net, Selected Normalized Data, Target); |
| (44) | Test Model using IDS-Net |
| (45) | Result = sim (IDS-Net, Test Data) |
| (46) | CP = Calculate Parameters (Result, Unique Label) |
| (47) | Return: CP as Classification Parameters |
| (48) | End–Algorithm |
