Swarm intelligence
Parameter Selection in Particle Swarm Optimization
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
A review of particle swarm optimization. Part I: background and development
Natural Computing: an international journal
Natural Computing: an international journal
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
Engineering Applications of Artificial Intelligence
Super-fit control adaptation in memetic differential evolution frameworks
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
A survey of particle swarm optimization applications in electric power systems
IEEE Transactions on Evolutionary Computation
Simplifying Particle Swarm Optimization
Applied Soft Computing
Neighborhood topologies in fully informed and best-of-neighborhood particle swarms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Particle swarm optimization (PSO) has been widely used in optimization problems. If an identification problem can be transformed into an optimization problem, PSO can be used to identify the unknown parameters in a nonlinear model that is used to describe a system. Currently, most PSO based identification or optimization solutions can only be implemented offline. The difficulties of online implementation mainly come from the unavoidable lengthy simulation time to evaluate a candidate solution. In this paper, a technique for faster than real-time simulation is introduced and implementation details of PSO based identification algorithm is presented. Performance of the proposed technique is demonstrated through application to parameters identification of permanent magnet synchronous machine control system. The algorithm is implemented in Matlab/Simulink with the most fundamental blocks and Embedded Matlab Functions. Thus the program can be compiled to C/C++ code through Real-time Workshop and be able to run on hardware controllers like dSPACE. The proposed techniques can also be applied to many other online identification and optimization problems.