Particles Prefer Walking Along the Axes: Experimental Insights into the Behavior of a Particle Swarm
This addresses stagnation in PSO, a common meta-heuristic for continuous optimization, offering an incremental improvement for practitioners.
The paper investigates why particle swarm optimization (PSO) stagnates at non-optimal points, finding that swarms often enter unbalanced states where potential concentrates on one axis, blinding them to improvements in other directions. It proposes a slightly adapted PSO that rebalances the potential, increasing solution quality.
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 non-optimal points. To achieve our results, we use the newly defined potential 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 solution.