Particle Swarm Optimization (PSO)
The Particle Swarm Optimization algorithm [6] is one of the most
popular swarm-optimization techniques. Each particle represents a
candidate solution and has its own position \(X\) and velocity \(V\).
Initially, these values are randomly defined according to the boundaries
of the problem. As the time (iterations) goes by, the particles move
according to its velocity and reach a new position. This velocity of a
particle, see equation (4), is a linear combination of: the particle’s
inertia, through coefficient ω; its memory, through coefficient\(c_{1}\); and some social interaction with the group, through
coefficient \(c_{2}\). The best position found by the group, G ,
and the best position ever reached by the particle, P , are used
to update its velocity. In addition, random values, \(\text{rand}_{1}\)and \(\text{rand}_{2}\), are used to boost good exploitation abilities
of the search space. Equations (4) and (5), used to update the
particles’ positions, describe the complete iteration process.