The flowerpollinationalgorithm (FPA) was developed and proposed as one of the nature-inspired population-based metaheuristic optimization techniques for solving optimization problems. In this paper, the enhanced ver...
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The flowerpollinationalgorithm (FPA) was developed and proposed as one of the nature-inspired population-based metaheuristic optimization techniques for solving optimization problems. In this paper, the enhanced version of the original FPA called the modified flower pollination algorithm (MoFPA) is presented to improve the search performance. The switching probability of the original FPA used for selection between local and global pollination is changed from the fixed manner to the random manner. The proposed MoFPA is tested against five benchmark optimization problems compared with the original FPA. Then, the proposed MoFPA is applied to the proportional-integral-derivative-accelerated (PIDA) controller design optimization for the electric furnace temperature control system. System responses obtained by the PIDA controller designed by the MoFPA will be compared with those obtained by the PID controller designed by the MoFPA. As results, the proposed MoFPA performs more efficient and more robust in global optimum finding of five selected benchmark optimization problems with higher success rates than the original FPA. In addition, the PIDA controller designed by the proposed MoFPA can provide the very satisfactory tracking and regulating responses of the electric furnace temperature control system superior to the PID controller.
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