Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A particle swarm optimization approach to nonlinear rational filter modeling
Expert Systems with Applications: An International Journal
PSO-based single multiplicative neuron model for time series prediction
Expert Systems with Applications: An International Journal
Applying particle swarm optimization algorithm to roundness measurement
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Current sharing of paralleled DC-DC converters using GA-based PID controllers
Expert Systems with Applications: An International Journal
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
Parameter identification of chaotic dynamic systems through an improved particle swarm optimization
Expert Systems with Applications: An International Journal
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
Expert Systems with Applications: An International Journal
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This paper presents a novel improved fuzzy particle swarm optimization (IFPSO) algorithm to the intelligent identification and control of a dynamic system. The proposed algorithm estimates optimally the parameters of system and controller by minimizing the mean of squared errors. The particle swarm optimization is enhanced intelligently by using a fuzzy inertia weight to rationally balance the global and local exploitation abilities. In the proposed IFPSO, every particle dynamically adjusts inertia weight according to particles best memories using a nonlinear fuzzy model. As a result, the IFPSO algorithm has a faster convergence speed and a higher accuracy. The performance of IFPSO algorithm is compared with advanced algorithms such as Real-Coded Genetic Algorithm (RCGA), Linearly Decreasing Inertia Weight PSO (LDWPSO) and Fuzzy PSO (FPSO) in terms of parameter accuracy and convergence speed. Simulation results demonstrate the effectiveness of the proposed algorithm.