The segmentation of digital images is an open problem that has increasingly attracted the attention of researchers during the last years. Thresholding approaches are often used due to their independence from the resol...
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The segmentation of digital images is an open problem that has increasingly attracted the attention of researchers during the last years. Thresholding approaches are often used due to their independence from the resolution of the images and their speed. However, simple thresholding approaches usually generate low-quality images. To achieve a better balance between speed and quality, many criteria are used to select the thresholds that segment the image. The type II fuzzy entropy (TII-FE) was introduced to perform image thresholding by modeling the classes of an image as membership functions to avoid uncertainty on the selection of the thresholds leading to improvement regarding the quality of the segmented image. To maximize the TII-FE, an efficient optimizer should be used to converge quickly to the optimal. In this paper, a hybrid method based on the paddy field algorithm (PFA) and the Plant Propagation algorithm (PPA) with the disruption operator (HPFPPA-D) is presented for the maximization of the TII-FE. The hybridization of these algorithms is used to enhance the performance of each algorithm by introducing operators from other approaches. In this case, the PFA shows good exploitation features that are complemented by the exploration behavior of PPA and refined with the disruption operator. The synergy between those methods has led to an accurate methodology for TII-FE thresholding. The proposed HPFPPA-D for TII-FE is evaluated using a set of benchmark images regarding convergence and image quality. The results are compared against other state-of-the-art evolutionary algorithms providing evidence of a superior and significant performance.
paddy field algorithm (PFA) is a fast random algorithm with global search capability. But when the number of solutions is over the range, the algorithm efficiency becomes low because it executes a lot of redundant ite...
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ISBN:
(纸本)9781467322379
paddy field algorithm (PFA) is a fast random algorithm with global search capability. But when the number of solutions is over the range, the algorithm efficiency becomes low because it executes a lot of redundant iterations. Pattern search algorithm is sensitive to the initial condition. In order to strength the local search ability, paddy field algorithm is introduced and a novel paddy field algorithm is proposed in this paper. The hybrid algorithm operates by initially scattering seeds at random in the parameter space. The final result is found by pattern search based on the result of PFA algorithm. This algorithm is tested on three sample functions alongside the basic paddy field algorithm and pattern search method. The simulation results show that the algorithm performs well.
This paper uses a new bionic algorithm in turning of PID *** paddy field algorithm (PFA)is operat-ed in the parametric space from the initial scattering of seeds. The number of seeds of every plant depends on the func...
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ISBN:
(纸本)9781457702686
This paper uses a new bionic algorithm in turning of PID *** paddy field algorithm (PFA)is operat-ed in the parametric space from the initial scattering of seeds. The number of seeds of every plant depends on the function val-ue,as the plant,the closest optimal solution,produces the most *** produced seeds also depend on the number of the plant's neighbors,for only a percentage is viable due to ***,the seeds of each plant must be dispersed in or-der to prevent being stuck in local *** thus applied this improved algorithm to design the PID controller of a high-order *** effect of various parameters on the performance of the algorithm was also *** performance is tested with the PID and PSO *** results show that the approach is effective and the designed controller has a better performance of overshoot and settling time.
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