EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Predicted modified PSO with time-varying accelerator coefficients
International Journal of Bio-Inspired Computation
Adaptive particle swarm optimization with feedback control of diversity
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
An approach to multimodal biomedical image registration utilizing particle swarm optimization
IEEE Transactions on Evolutionary Computation
Stability analysis of the particle dynamics in particle swarm optimizer
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
IEEE Transactions on Evolutionary Computation
International Journal of Intelligent Information and Database Systems
International Journal of Intelligent Information and Database Systems
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Inspired by the information prediction existing in the nature intelligent agents, the author has developed a modified particle swarm optimisation (PSO) with a forward PD controller (SPSO-FWPD) earlier. Comprehensive analysis for the model is provided in the paper, including its stabilisation, convergence and dynamic behaviour. Later, another modified PSO with a feedback PD controller (SPSO-FBPD) is presented accompanying some analysis. The introductions of different PD controllers develop the standard PSO (SPSO) with information prediction capability, which can guide the particle to respond to the change of their exemplars correctly and rapidly, and greatly contributes to a successful global search. The proposed methods provide some new ideas for the improvement of SPSO, and are compared with SPSO based on some numerical optimisation simulations. The relative experimental results show SPSO with different PD controller performs better than SPSO on the complex optimisation problems.