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This paper proposes new optimization algorithms for the optimal tuning of PI controllers dedicated to a class of second-order processes with integral component and variable parameters. The sensitivity analysis with respect to the parametric variations of the controlled process leads to the sensitivity models. The augmentation of the output sensitivity functions over the integral of absolute error...
In this paper, the results for the CEC 2010 Competition and Special Session on Constrained Real-Parameter Optimization using the multiobjective differential evolution algorithm with spherical pruning (sp-MODE) are presented. According to the obtained results, the sp-MODE shows to be able to find feasible solutions in highly constrained search spaces.
The effect of the parameter values in GA is undeniable. Different working operators as well as different values for the parameters would result in different efficiency and performance for a given problem. The effect of parameters on exploration and exploitation is the main effective factor in GAs. Keeping a balance between the exploration and exploitation during the run would lead to good results...
This paper proposes a new fuzzy tuned inertia weight particle swarm optimization (FIPSO) which remarkably outperforms the standard PSO, previous fuzzy as well as adaptive based PSO methods. Two benchmark functions with asymmetric initial range settings are used to validate the proposed algorithm and compare its performance with those of the other tuned parameter PSO algorithms. Numerical results indicate...
This paper presents a modified shuffled frog leaping algorithm (SFLA) for optimal tuning of proportional-integral-derivative (PID) controller gains for multivariable processes. The SFLA is a meta-heuristic search method inspired from the memetic evolution of a group of frogs when seeking for food. It consists of a frog leaping rule for local search and a memetic shuffling rule for global information...
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