Machine Learning
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Exposing origin-seeking bias in PSO
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Mean and variance of the sampling distribution of particle swarm optimizers during stagnation
IEEE Transactions on Evolutionary Computation
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Stability analysis of the particle dynamics in particle swarm optimizer
IEEE Transactions on Evolutionary Computation
Interior ballistic charge design based on a modified particle swarm optimizer
Structural and Multidisciplinary Optimization
Information Sciences: an International Journal
Particles prefer walking along the axes: experimental insights into the behavior of a particle swarm
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Computational Optimization and Applications
| Hi-index | 0.00 |
The most common versions of particle swarm optimization PSO algorithms are rotationally variant. It has also been pointed out that PSO algorithms can concentrate particles along paths parallel to the coordinate axes. In this paper, the authors explicitly connect these two observations by showing that the rotational variance is related to the concentration along lines parallel to the coordinate axes. Based on this explicit connection, the authors create fitness functions that are easy or hard for PSO to solve, depending on the rotation of the function.