Swarm intelligence
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
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
Convex Optimization
Is Entropy Suitable to Characterize Data and Signals for Cognitive Informatics?
ICCI '04 Proceedings of the Third IEEE International Conference on Cognitive Informatics
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A unified approach to fractal dimensions
ICCI '05 Proceedings of the Fourth IEEE International Conference on Cognitive Informatics
The Standard Particle Swarm Optimization Algorithm Convergence Analysis and Parameter Selection
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
Performance Evaluation of Multisatge Service System Using Matrix Geometric Method
ICSNC '09 Proceedings of the 2009 Fourth International Conference on Systems and Networks Communications
Comparison among five evolutionary-based optimization algorithms
Advanced Engineering Informatics
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
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
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This paper examines the inherited persistent behavior of particle swarm optimization and its implications to cognitive machines. The performance of the algorithm is studied through an average particle's trajectory through the parameter space of the Sphere and Rastrigin function. The trajectories are decomposed into position and velocity along each dimension optimized. A threshold is defined to separate the transient period, where the particle is moving towards a solution using information about the position of its best neighbors, from the steady state reached when the particles explore the local area surrounding the solution to the system. Using a combination of time and frequency domain techniques, the inherited long-term dependencies that drive the algorithm are discerned. Experimental results show the particles balance exploration of the parameter space with the correlated goal oriented trajectory driven by their social interactions. The information learned from this analysis can be used to extract complexity measures to classify the behavior and control of particle swarm optimization, and make proper decisions on what to do next. This novel analysis of a particle trajectory in the time and frequency domains presents clear advantages of particle swarm optimization and inherent properties that make this optimization algorithm a suitable choice for use in cognitive machines.