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
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
Parameter identification of chaotic dynamic systems through an improved particle swarm optimization
Expert Systems with Applications: An International Journal
Parameters identification of nonlinear state space model of synchronous generator
Engineering Applications of Artificial Intelligence
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
A chaotic digital secure communication based on a modified gravitational search algorithm filter
Information Sciences: an International Journal
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Heuristic optimization has shown its superiority in handling identification problem of complicated system, for methods based on heuristic optimization do not have special requirements on model structures of the target system. In this paper, a piecewise function based gravitational search algorithm (PFGSA) is proposed and applied in parameter identification of automatic voltage regulator (AVR) system. In the proposed algorithm, a piecewise function is designed as the gravitational constant function to replace the traditional exponential equation. The piecewise function provides more rational gravitational constant to control the convergence of algorithm, and thus excellent searching ability is likely to be achieved. Moreover a new weighted objective function is proposed in the identification frame. Comparative experimental studies are conducted to test the searching ability of PFGSA and to verify the performance of proposed identification strategy, while genetic algorithm, particle swarm optimization and GSA are employed for comparison. The experimental results show that PFGSA performs the best on term of accuracy and stability in the parameter identification of AVR system, and the proposed identification strategy is effective.