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Particle swarm optimization (PSO) is a widely used nature-inspiredmeta-heuristic for solving continuous optimization problems. However, whenrunning the PSO algorithm, one encounters the phenomenon of so-calledstagnation, that means in our context, the whole swarm starts to converge to asolution that is not (even a local) optimum. The goal of this work is to pointout possible reasons why the swarm stagnates at these non-optimal points. Toachieve our results, we use the newly defined potential of a swarm. The totalpotential has a portion for every dimension of the search space, and it dropswhen the swarm approaches the point of convergence. As it turns outexperimentally, the swarm is very likely to come sometimes into "unbalanced"states, i. e., almost all potential belongs to one axis. Therefore, the swarmbecomes blind for improvements still possible in any other direction. Finally,we show how in the light of the potential and these observations, a slightlyadapted PSO rebalances the potential and therefore increases the quality of thesolution.

Particles Prefer Walking Along the Axes: Experimental Insights into the Behavior of a Particle Swarm