An Improved Grey Wolf Optimizer Based on Differential Evolution and OTSU Algorithm

Xiaokang Zhou,Haiye Yu,Yuanyuan Liu,Jiahui Sun,Yueyong Wang

doi:10.3390/app10186343

Xiaokang Zhou, Haiye Yu + Show 3 more

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https://doi.org/10.3390/app10186343

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Abstract

Aimed at solving the problems of poor stability and easily falling into the local optimal solution in the grey wolf optimizer (GWO) algorithm, an improved GWO algorithm based on the differential evolution (DE) algorithm and the OTSU algorithm is proposed (DE-OTSU-GWO). The multithreshold OTSU, Tsallis entropy, and DE algorithm are combined with the GWO algorithm. The multithreshold OTSU algorithm is used to calculate the fitness of the initial population. The population is updated using the GWO algorithm and the DE algorithm through the Tsallis entropy algorithm for crossover steps. Multithreshold OTSU calculates the fitness in the initial population and makes the initial stage basically stable. Tsallis entropy calculates the fitness quickly. The DE algorithm can solve the local optimal solution of GWO. The performance of the DE-OTSU-GWO algorithm was tested using a CEC2005 benchmark function (23 test functions). Compared with existing particle swarm optimizer (PSO) and GWO algorithms, the experimental results showed that the DE-OTSU-GWO algorithm is more stable and accurate in solving functions. In addition, compared with other algorithms, a convergence behavior analysis proved the high quality of the DE-OTSU-GWO algorithm. In the results of classical agricultural image recognition problems, compared with GWO, PSO, DE-GWO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm had accuracy in straw image recognition and is applicable to practical problems. The OTSU algorithm improves the accuracy of the overall algorithm while increasing the running time. After adding the DE algorithm, the time complexity will increase, but the solution time can be shortened. Compared with GWO, DE-GWO, PSO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm has better results in segmentation assessment.

Highlights

Image segmentation is an important part of image processing in practice [1,2,3,4,5], as well as a key image analysis technique [6,7,8,9,10]
Multithreshold OTSU calculates the fitness in the initial population and makes the initial stage basically stable
The search results are closer to the real optimal solution. These results demonstrate the superior performance of differential evolution (DE)−OTSU−grey wolf optimizer (GWO) in finding optimal solutions

Summary

Introduction

Image segmentation is an important part of image processing in practice [1,2,3,4,5], as well as a key image analysis technique [6,7,8,9,10]. The threshold image segmentation method divides pixels into several classes by setting the threshold value to realize the separation of the object and the background in the image. Threshold segmentation based on a histogram is the most widely used image segmentation method. A better threshold is selected according to the relationship between the peaks and troughs of the histogram [11,12,13]. Otsu, and Kapur are the most common methods.

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