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

Liu, Yuan Yuan; Sun, Jiahui; Yu, Hao; Wang, Yueyong; Zhou, X. K.

doi:10.3390/app10186343

Cited by 27 publications

(22 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The OTSU threshold segmentation method can be extended from single threshold to multithreshold segmentation. Multithreshold segmentation adopts different thresholds to segment the image into different regions or targets [62]. Applying intelligent algorithm to multithreshold search can greatly speed up the algorithm [63].…”

Section: Gwo-otsu Multithreshold Segmentation Algorithmmentioning

confidence: 99%

What Is the Effect of Quantitative Inversion of Photosynthetic Pigment Content in Populus euphratica Oliv. Individual Tree Canopy Based on Multispectral UAV Images?

Kahaer

Shi

et al. 2022

Forests

View full text Add to dashboard Cite

It is highly necessary to apply unmanned aerial vehicle (UAV) remote sensing technology to forest health assessment. To prove the feasibility of quantitative inversion of photosynthetic pigment content (PPC) in Populus euphratica Oliv. individual tree canopy (PeITC) by using multispectral UAV images, in this study, Parrot Sequoia+ multispectral UAV system was manipulated to collect the images of Populus euphratica (Populus euphratica Oliv.) sample plots in Daliyabuyi Oasis from 2019 to 2020, and the canopy PPCs of five Populus euphratica sample trees per plot were determined in six plots. The Populus euphratica crown regions were extracted by grey wolf optimizer-OTSU (GWO-OTSU) multithreshold segmentation algorithm from the normalized difference vegetation index (NDVI) images of Populus euphratica sample plots obtained after preprocessing, and the PeITCs were segmented by multiresolution segmentation algorithm. The mean values of 27 spectral indices in the PeITCs were calculated in each plot, and the optimal model was constructed for quantitative estimation of the PPCs in the PeITCs, then the inversion results were compared and verified based on GF-6 and ZY1-02D satellite imageries respectively. The results were as follows. (1) The average value of canopy chlorophyll content (Chl) was 2.007 mg/g, the mean value of canopy carotenoid content (Car) was 0.703 mg/g. The coefficient of variation (C.V) of both were basically the same and they were both of strong variability. The measured PPCs of the PeITCs in Daliyabuyi Oasis was generally low. The average contents of chlorophyll and carotenoid in PeITC in June were more than twice those in August, while the mean ratio between them was significantly lower in June than in August. The measured PPCs had no obvious spatial distribution law. However, that could prove the rationality of sample selection in this study. (2) NDVI had the best effect of highlighting vegetation among all quadrats in the study area. Based on the GWO-OTSU multithreshold segmentation method, the canopy area of Populus euphratica could be quickly and effectively extracted from the quadrat NDVI map. The best segmentation effect of PeITCs was obtained based on a multiresolution segmentation method when the segmentation scale was 120, the shape index was 0.7, and the compactness index was 0.5. Compared with manual vectorization method of visual interpretation, the root mean square error (RMSE) and Pearson correlation coefficient (R) values of the mean NDVI values in PeITCs obtained by these two methods were 0.038 and 0.951. (3) Only 12 of the 27 spectral indices were significantly correlated with Chl and Car at the significance level of 0.02. Characteristics of the calibration set and validation set were basically consistent with those of the entire set. The classification and regression tree-decision tree (CART-DT) model performed best in the estimation of the PPCs in the PeITCs, in which, when estimating the Car, the calibration coefficient of determination (R2C) was 0.843, the calibration root mean square error (RMSEC) was 0.084, the calibration residual prediction deviation (RPDC) was 2.525, the validation coefficient of determination (R2V) was 0.670, the validation root mean square error (RMSEV) was 0.251, the validation residual prediction deviation (RPDV) was 1.741. (4) Qualitative comparison of spectral reflectance and NDVI values between GF-6 multispectral imagery and Parrot Sequoia+ multispectral image on the 172 PeITCs can show the reliability of Parrot Sequoia+ multispectral image. The comparison results of five PeITCs relative health degree judged by field vision judgment, measured SPAD value, predicted value of Chl (Chlpre), the red edge value calculated by ZY1-02D (ZY1-02Dred edge) and the Carotenoid Reflection Index 2 (CRI2) value calculated by ZY1-02D (ZY1-02DCRI2) can further prove the scientificity of inversion results to a certain extent. These results indicate that multispectral UAV images can be applied for quantitative inversion of PPC in PeITC, which could provide an indicator for the construction of a Populus euphratica individual tree health evaluation indicator system based on UAV remote sensing technology in the next step.

show abstract

Section: Gwo-otsu Multithreshold Segmentation Algorithmmentioning

confidence: 99%

What Is the Effect of Quantitative Inversion of Photosynthetic Pigment Content in Populus euphratica Oliv. Individual Tree Canopy Based on Multispectral UAV Images?

Kahaer

Shi

et al. 2022

Forests

View full text Add to dashboard Cite

show abstract

“…In order to further improve the performance of GWO and avoid the problem of suffering premature convergence due to stagnation at suboptimal solutions, a new algorithm GLF-GWO [41] was proposed with the Levy-flight search mechanism to enhance the search efficiency of leading hunters. DE-OTSU-GWO [42] uses the differential evolution algorithm and the OTSU algorithm [43] to solve the problems of poor stability and easily falling into the local optimal solution. These methods are only used for numerical continuous space, but the community detection is discrete search space.…”

Section: Gwomentioning

confidence: 99%

An Improved Gray Wolf Optimization Algorithm with a Novel Initialization Method for Community Detection

Kang

Wang

et al. 2022

Mathematics

View full text Add to dashboard Cite

Community discovery (CD) under complex networks is a hot discussion issue in network science research. Recently, many evolutionary methods have been introduced to detect communities of networks. However, evolutionary optimization-based community discovery still suffers from two problems. First, the initialization population quality of the current evolutionary algorithm is not good, resulting in slow convergence speed, and the final performance needs to be further improved. Another important issue is that current methods of CD have inconsistent network detection performance at different scales, showing a dramatic drop as the network scale increases. To address such issues, this paper proposes an algorithm based on the novel initial method and improved gray wolf optimization (NIGWO) to tackle the above two problems at the same time. In this paper, a novel initialization strategy is proposed to generate a high-quality initial population and greatly accelerate the convergence speed of population evolution. The strategy effectively fused the elite substructure of the community and different features based on the dependency and other features among nodes. Moreover, an improved GWO is presented with two new search strategies. An improved hunting prey stage is proposed to retain the excellent substructures of populations and quickly improve the community structure. Furthermore, new mutation strategies from node level to community level are designed in an improved encircling prey stage. Specifically, boundary nodes are mutated according to a proposed function to improve the search efficiency and save the computation assumption. Numerous experiments have proven our method obtains more excellent performance in most networks compared with 11 state-of-the-art algorithms.

show abstract

“…Researchers are trying to improve the performance of these meta heuristic techniques continuously and for that reason hybridization of two or more such algorithms are in the research field. Few hybrid GWO techniques can be found in [2], [3], [4]. There is a hybrid GWO algorithm introduced by combining the classic GWO algorithm and Fitness based Self Adaptive Differential Evolution (FSADE) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Advancement on Grey Wolf Optimization with Fitness Based Self Adaptive Differential Evolution

Mohanta¹,

Sharma²

2023

IJSREM

View full text Add to dashboard Cite

Hybridization of two or more variants of algorithms is the recent trend of the research field. With the help of a hybridized algorithm we try to find out the better optimal solution and to solve various optimization applications. In this paper, a new approach Advancement on Grey Wolf Optimization with Fitness Based Self Adaptive Differential Evolution (AGWO-FSADE) is proposed. Fitness of populations are calculated using the self adaptive strategy of FSADE and updated by GWO algorithm. FSADE algorithms balance the convergence and diversity capability and due to fine tuning of Crossover Probability CR and Scale Factor F therefore, in large step size very less chance to skip the actual solutions. The performance of AGWO-FSADE is measured by 19 Benchmark functions and compared with classic GWO, ABC and PSOGWO algorithm. The results are more accurate to solve these functions. Keywords - nature inspired algorithm, self adaptive technique, grey wolf optimizer, fitness based self adaptive differential evolution, optimization technique

show abstract

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

Cited by 27 publications

References 33 publications

What Is the Effect of Quantitative Inversion of Photosynthetic Pigment Content in Populus euphratica Oliv. Individual Tree Canopy Based on Multispectral UAV Images?

What Is the Effect of Quantitative Inversion of Photosynthetic Pigment Content in Populus euphratica Oliv. Individual Tree Canopy Based on Multispectral UAV Images?

An Improved Gray Wolf Optimization Algorithm with a Novel Initialization Method for Community Detection

Advancement on Grey Wolf Optimization with Fitness Based Self Adaptive Differential Evolution

Contact Info

Product

Resources

About