Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Fuzzy Adaptive Turbulent Particle Swarm Optimization
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
The landscape adaptive particle swarm optimizer
Applied Soft Computing
IPC '07 Proceedings of the The 2007 International Conference on Intelligent Pervasive Computing
Dispersed particle swarm optimization
Information Processing Letters
A fuzzy adaptive turbulent particle swarm optimisation
International Journal of Innovative Computing and Applications
Ant colony and particle swarm optimization for financial classification problems
Expert Systems with Applications: An International Journal
A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification
Advances in Engineering Software
Expert Systems with Applications: An International Journal
An Adaptive Particle Swarm Optimizer Using Balanced Explorative and Exploitative Behaviors
SYNASC '08 Proceedings of the 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Tackling magnetoencephalography with particle swarm optimization
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Integral-controlled particle swarm optimization (ICPSO) is an effective variant of particle swarm optimization (PSO) aiming to increase the population diversity. Due to the additional accelerator items, the behavior of ICPSO is more complex, and provides more chances to escaping from a local optimum than the standard version of PSO. However, many experimental results show the performance of ICPSO is not always well because of the particles' un-controlled movements. Therefore, a new variant, integral particle swarm optimization with dispersed accelerator information (IPSO-DAI) is designed to improve the computational efficiency. In IPSO-DAI, a predefined predicted velocity index is introduced to guide the moving direction. If the average velocity of one particle is superior to the index value, it will choice a convergent manner, otherwise, a divergent manner is employed. Furthermore, the choice of convergent manner or divergent manner for each particle is associated with its performance to fit different living experiences. Simulation results show the proposed variant is more effective than other three variants of particle swarm optimization especially for multi-modal numerical problems. The IPSO-DAI algorithm is also applied to directing the orbits of discrete chaotic dynamical systems by adding small bounded perturbations, and achieves the best performance among four different variants of PSO.