Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
On the Convergence of a Population-Based Global Optimization Algorithm
Journal of Global Optimization
A hybrid electromagnetism-like algorithm for single machine scheduling problem
Expert Systems with Applications: An International Journal
Design of fractional-order PIλDµ controllers with an improved differential evolution
Engineering Applications of Artificial Intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems
IEEE Transactions on Neural Networks
Circle detection using electro-magnetism optimization
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
Optimum design of fractional order PIλDµ controller for AVR system using chaotic ant swarm
Expert Systems with Applications: An International Journal
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Based on the electromagnetism-like algorithm, an evolutionary algorithm, improved EM algorithm with genetic algorithm technique (IEMGA), for optimization of fractional-order PID (FOPID) controller is proposed in this article. IEMGA is a population-based meta-heuristic algorithm originated from the electromagnetism theory. It does not require gradient calculations and can automatically converge at a good solution. For FOPID control optimization, IEMGA simulates the ''attraction'' and ''repulsion'' of charged particles by considering each controller parameters as an electrical charge. The neighborhood randomly local search of EM algorithm is improved by using GA and the competitive concept. IEMGA has the advantages of EM and GA in reducing the computation complexity of EM. Finally, several illustration examples are presented to show the performance and effectiveness.