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

被引:26
|
作者
Liu, Yuanyuan [1 ,2 ]
Sun, Jiahui [1 ]
Yu, Haiye [3 ]
Wang, Yueyong [4 ]
Zhou, Xiaokang [1 ]
机构
[1] Jilin Agr Univ, Coll Informat Technol, Changchun 130118, Peoples R China
[2] Oklahoma State Univ, Dept Biosyst & Agr Engn, Stillwater, OK 74078 USA
[3] Jilin Univ, Minist Educ, Key Lab Bion Engn, Changchun 130025, Peoples R China
[4] Jilin Agr Univ, Coll Engn & Technol, Changchun 130118, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2020年 / 10卷 / 18期
关键词
grey wolf optimizer; differential evolution; multithreshold OTSU; tsallis entropy; image segmentation; straw coverage detection; IMAGE SEGMENTATION;
D O I
10.3390/app10186343
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
引用
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页数:20
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