PARTICLE SWARN OPTIMIZATION SUMMARY

 Particle swarm optimization can be summarized as the following steps:

(1)The position and velocity of all particles (population size n) and the basic parameters of the algorithm are initialized.(2)Taking the initial position of each particle as individual extremum, the initial fitness value of each particle in the population is calculated, and the optimal position of the current population is obtained.(3)Update the speed and position of each particle.(4)Compare the current fitness value with the historical optimal value. If the current fitness value is superior to the historical optimal value, the historical optimal value of the particle is set to the current fitness value, and the particle position is updated.(5)If the current fitness value is better than the historical population optimal value, the historical population optimal value is set as the current fitness value to update the global optimal solution of the population.(6)Check the end conditions. If the optimization results meet the conditions for the iteration stop, the optimal solution is obtained and the iteration is terminated. Otherwise, turn back to step 4 and continue the optimization until the iteration stop condition is met.