A Review on Particle Swarm Optimization Algorithm and Its Variants to Human Motion Tracking

Algorithm 1

Pseudocode for particle swarm optimization (PSO).

Set parameters .

// Initialization

Initialize a population of particles with random position () and velocity ().

foreach Particle do

Compute the fitness value .

end for

Initialize the inertia weight .

Select the best particle in the swarm .

// Iteration process

for to maximum number of iterations do

 foreach particle do

   update velocity and position for the particles.

  employ the inertia weight update rule.

   compute particles fitness value .

   update best particles: and .

  end for

  if convergence criteria are met then

  Exit from iteration process;

end if