Algorithm 1. Proposed HNIDS method (EGA-with PSO)

Input: attributes set, Random population (RP), the maximum number of generations (Max_g), Binary vector Output: Optimized individual generation RP(n)

Step1: Apply RF method to determine the best fitness value for each participant

1.1 for each participant i to RP

1.2 do

1.3 determine the best fitness (i)

1.3.1 call random_forest ();

Step 2: Apply the enhanced selection process of GA

2.1 while (iteration_count< n)

2.2 call selection (i);

2.2.1 set New_selected_value = selection (i);

Step 3: Select best bit individuals using enhanced mutation and crossover (EGA)

3.1 Choose the best fit

3.1.1 if (New_selected_value == Best_fit), than

3.2 if (Crossover_generation) than

3.2.1 Choose two parents randomly (i_pa, i_pb)

3.2.2 Generates offspring parent (i_pc) = Crossover_generation (i_pa, i_pb);

3.3 else

3.4 Call enhanced mutation process

3.4.1 randomly select an independent value (i) from the parent set

3.4.2 Generates offspring parent (i_pc) = Crossover_generation (i_pa, i_pb);

3.5 else

Step 4: Calculate the best fit (fitness value) for each participant

4.1 if (New_selected_value == Best_fit)

4.1.1 Process Best_fit

4.2 else

4.2.1 Interchange the least fit participants with offspring parent (i_pc)

4.3 else

Step 5: Apply the PSO method to check the outcome best fir

5.1 New_Output_PSO = PSO_Rejected (offspring parent (i_pc))

5.2 End

Step 6: apply enhanced selection to generate a new population

6.1 if (New_Output_PSO == Best_fit) then

6.2 New_Output_PSO = New_Output_PSO + Current_PSO_Output

6.3 else

6.4 Call Enhanced_selection ();

6.5 End