Detection of Human Stress Using Optimized Feature Selection and Classification in ECG Signals

Algorithm 1

Pseudocode of AVO Algorithm.

Inputs: n-number of vultures in an environment (population) and number of iterations (T)

Outputs: best vulture location and its fitness function

  (1) Initialization of random population P_i (i = 1, 2, …, N)

  (2) While (stop condition not found) do

  (3) Evaluate fitness function

  (4) Fix first best vulture as P_BestVul1 // First best location

  (5) Fix second best vulture as P_BestVul2 // Second best location

  (6) for (every vulture (P_i)) do // P_i denotes the current vector location of the vulture

  (7) choose R(i) as the best vulture by using the below equation

  (8)

  (9) Update satisfied vultures (F) by using the below equation

  (10)

  (11) if (|F| ≥ 1) then

  (12) if (P₁ ≥ random P₁) then

  (13) Update vulture’s position by

  (14) else

  (15) Evaluate vulture’s position using the below equation

  (16)

  (17) if (|F| < 1) then

  (18) if (|F| ≥ 0.5) then

  (19) if (P₂ ≥ random P₂) then

  (20) Calculate vulture’s position using the below equation

  (21)

  (22) where d(t)represents the distance between a vulture and one of the best vultures

  (23) else

  (24) Estimate vulture’s position using the below equation

  (25) //

  (26) Where and

  (27) else

  (28) if (P₃ ≥ random P₃) then

  (29) Evaluate vulture’s position using the below equation

  (30)

  (31) else

  (32) Estimate vulture’s position using the below equation

  (33)

  (34) Return P_BestVul1