Question

Difference between pso and other swarm intelligence algorithms

Answer 1

Particle Swarm Optimization (PSO) is a versatile population-based optimization technique, in many respects similar to evolutionary algorithms. The PSO has its origin on the swarm intelligence algorithms, which are concerned with the design of intelligent multi-agent systems by taking stimulation from the collective behavior of social insects such as ants, termites, bees, and wasps, as well as from other animal societies such as flocks of birds or schools of fish. In PSO method, the particles that represent potential solutions move around in the phase space with a velocity updated by the particle’s own experience and the experience of the particle’s neighbors or the experience of the whole swarm.

Particle Filter (PF) performs sequential Monte Carlo estimation (PSO is not Monte Carlo method) based on particle representation of probability densities, by representing the posterior density function by a set of random samples with associated weights and computing estimates based on these samples and weights. In this method the particles with high weights propagate and the particles with smallest weights are eliminated by a re-sampling procedure.