The theory of evolution strategies
The theory of evolution strategies
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Tight Bounds On Expected Order Statistics
Probability in the Engineering and Informational Sciences
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Information Processing Letters
Algorithmic analysis of a basic evolutionary algorithm for continuous optimization
Theoretical Computer Science
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
Stability analysis of social foraging swarms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Circle detection using electro-magnetism optimization
Information Sciences: an International Journal
Convergence time analysis of particle swarm optimization based on particle interaction
Advances in Artificial Intelligence
Review: A parameter selection strategy for particle swarm optimization based on particle positions
Expert Systems with Applications: An International Journal
| Hi-index | 5.23 |
In this paper, we analyze the behavior of particle swarm optimization (PSO) on the facet of particle interaction. We firstly propose a statistical interpretation of particle swarm optimization in order to capture the stochastic behavior of the entire swarm. Based on the statistical interpretation, we investigate the effect of particle interaction by focusing on the social-only model and derive the upper and lower bounds of the expected particle norm. Accordingly, the lower and upper bounds of the expected progress rate on the sphere function are also obtained. Furthermore, the sufficient and necessary condition for the swarm to converge is derived to demonstrate the PSO convergence caused by the effect of particle interaction.