This document describes a particle swarm optimization algorithm used to find the maximum likelihood estimation of parameters d, r0, r1, and r2. The algorithm initializes a swarm of particles within defined ranges for the parameters. It then iteratively updates the positions and velocities of particles based on their personal best positions and the global best position. The algorithm runs for 50 iterations, tracking the mean and variance of each parameter value at each iteration.