Particle swarm optimization (PSO)
is a widely used
nature-inspired meta-heuristic for solving
continuous optimization problems.
However, when running the PSO algorithm, one encounters the
phenomenon of so-called
stagnation, that means in our context, the whole swarm starts to converge
to a solution that is not (even a local) optimum.
The goal of this work is to point out
possible reasons why the swarm stagnates at these
To achieve our results, we use the newly
of a swarm.
The total potential has a portion for every dimension of
the search space, and it drops when
the swarm approaches the point of convergence.
As it turns out experimentally, the swarm is very likely to come
sometimes into “unbalanced” states, i.e., almost all
potential belongs to one axis. Therefore, the swarm becomes
blind for improvements still possible in any other direction.
Finally, we show how in the light of the potential and these
observations, a slightly adapted PSO rebalances
the potential and therefore increases the quality of the