Two-Phases Learning Shuffled Frog Leaping Algorithm

Zhao, Jia; Li, LV

doi:10.14257/ijhit.2015.8.5.22

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…The Fig. 2 depicted the boxplots for ISFLA, GSA [13], SMO [14] and DE [15]. The outcomes demonstrate that interquartile range and medians of ISFLA are low in the correlation of GSA, SMO and DE.…”

Section: B Analysis Of Resultsmentioning

confidence: 94%

Improved Shuffled Frog Leaping Algorithm

Jing

2017

IJASRE

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Section: B Analysis Of Resultsmentioning

confidence: 94%

Improved Shuffled Frog Leaping Algorithm

Jing

2017

IJASRE

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“…According to the articles [11,12,[16][17][18], H parameter is a random number that varies between 0 and 1. To improve the performance of the proposed algorithm and to highlight the influence of this parameter on the results obtained, we varied H as shown in the following Table 4.…”

Section: Influence Of Parameter H On the Segmentationmentioning

confidence: 99%

Unsupervised Segmentation of Images Based on Shuffled Frog-Leaping Algorithm

2017

J Inf Process Syst

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The image segmentation is the most important operation in an image processing system. It is located at the joint between the processing and analysis of the images. Unsupervised segmentation aims to automatically separate the image into natural clusters. However, because of its complexity several methods have been proposed, specifically methods of optimization. In our work we are interested to the technique SFLA (Shuffled Frog-Leaping Algorithm). It's a memetic meta-heuristic algorithm that is based on frog populations in nature searching for food. This paper proposes a new approach of unsupervised image segmentation based on SFLA method. It is implemented and applied to different types of images. To validate the performances of our approach, we performed experiments which were compared to the method of K-means.

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“…The clustering method based on intelligent optimization can convert a particular clustering problem to an optimization problem of the objective function, which finds the optimal value of the objective function to obtain the optimal clustering scheme through repeated iteration. The key technologies in using 1385 intelligent computing to solve the optimization problem include coding of the problem and design of an appropriate fitness function [11][12][13][14]. In this study, the shuffled leapfrog algorithm is combined with the K-means algorithm.…”

Section: Introductionmentioning

confidence: 99%