scholarly journals Intelligent Bio-Inspired Whale Optimization Algorithm for Color Image based Segmentation

2020 ◽  
Vol 28 (4) ◽  
Author(s):  
Athraa Jasim Mohammed ◽  
Khalil Ibrahim Ghathwan
Keyword(s):  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Color Image ◽  
Clustering Algorithms ◽  
Color Space ◽  
Input Image ◽  
Whale Optimization Algorithm ◽  
Color Image Segmentation ◽  
Local Optima ◽  
Whale Optimization

Color image segmentation is widely used methods for searching of homogeneous regions to classify them into various groups. Clustering is one technique that is used for this purpose. Clustering algorithms have drawbacks such as the finding of optimum centers within a cluster and the trapping in local optima. Even though inspired meta-heuristic algorithms have been adopted to enhance the clustering performance, some algorithms still need improvements. Whale optimization algorithm (WOA) is recognized to be enough competition with common meta-heuristic algorithms, where it has an ability to obtain a global optimal solution and avoid local optima. In this paper, a new method for color image based segmentation is proposed based on using whale optimization algorithm in clustering. The proposed method is called the whale color image based segmentation (WhCIbS). It was used to divide the color image into a predefined number of clusters. The input image in RGB color space was converted into L*a*b color space. Comparison of the proposed WhCIbS method was performed with the wolf color image based segmentation, cuckoo color image based segmentation, bat color image based segmentation, and k-means color image based segmentation over four benchmark color images. Experimental results demonstrated that the proposed WhCIbS had higher value of PSNR and lower value of RMSR in most cases compared to other methods.

scholarly journals Kapur’s Entropy for Color Image Segmentation Based on a Hybrid Whale Optimization Algorithm

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 318 ◽  
Author(s):  
Chunbo Lang ◽  
Heming Jia
Keyword(s):  
Image Segmentation ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Color Image ◽  
Similarity Index ◽  
Whale Optimization Algorithm ◽  
Color Image Segmentation ◽  
De Algorithm ◽  
Whale Optimization ◽  
Kapur’S Entropy

In this paper, a new hybrid whale optimization algorithm (WOA) called WOA-DE is proposed to better balance the exploitation and exploration phases of optimization. Differential evolution (DE) is adopted as a local search strategy with the purpose of enhancing exploitation capability. The WOA-DE algorithm is then utilized to solve the problem of multilevel color image segmentation that can be considered as a challenging optimization task. Kapur’s entropy is used to obtain an efficient image segmentation method. In order to evaluate the performance of proposed algorithm, different images are selected for experiments, including natural images, satellite images and magnetic resonance (MR) images. The experimental results are compared with state-of-the-art meta-heuristic algorithms as well as conventional approaches. Several performance measures have been used such as average fitness values, standard deviation (STD), peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), Wilcoxon’s rank sum test, and Friedman test. The experimental results indicate that the WOA-DE algorithm is superior to the other meta-heuristic algorithms. In addition, to show the effectiveness of the proposed technique, the Otsu method is used for comparison.

scholarly journals An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets

2021 ◽  
Vol 11 (2) ◽  
pp. 216-226
Author(s):  
Ayşe Nagehan Mat ◽  
Onur İnan ◽  
Murat Karakoyun
Keyword(s):  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Heuristic Algorithms ◽  
Clustering Algorithms ◽  
Whale Optimization Algorithm ◽  
Lévy Flight ◽  
Hunting Behavior ◽  
Levy Flight ◽  
Whale Optimization ◽  
Clustering Problems

Clustering, which is handled by many researchers, is separating data into clusters without supervision. In clustering, the data are grouped using similarities or differences between them. Many traditional and heuristic algorithms are used in clustering problems and new techniques continue to be developed today. In this study, a new and effective clustering algorithm was developed by using the Whale Optimization Algorithm (WOA) and Levy flight (LF) strategy that imitates the hunting behavior of whales. With the developed WOA-LF algorithm, clustering was performed using ten medical datasets taken from the UCI Machine Learning Repository database. The clustering performance of the WOA-LF was compared with the performance of k-means, k-medoids, fuzzy c-means and the original WOA clustering algorithms. Application results showed that WOA-LF has more successful clustering performance in general and can be used as an alternative algorithm in clustering problems.

Efficient Bag-of-Features using Improved Whale Optimization Algorithm for Histopathological Image Classification

2019 ◽  
Vol 12 (4) ◽  
pp. 269-279 ◽  
Author(s):  
Varun Tiwari ◽  
Sushil C. Jain
Keyword(s):  
Image Classification ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Whale Optimization Algorithm ◽  
Local Optima ◽  
Bag Of Features ◽  
Histopathological Image ◽  
Whale Optimization ◽  
Image Dataset ◽  
Histopathological Image Classification

Background: The whale optimization algorithm is one of the popular meta-heuristic algorithms which has successfully been applied in various application areas such as image analysis and data clustering. However, the slow convergence rate and chances of sticking into the local optima due to improper balance of its exploration and exploitation phases are some of its pitfalls. Therefore, in this paper, a new improved whale optimization algorithm has been proposed. Moreover, the proposed method has been used in bag-of-features method for histopathological image classification. Methods: The new algorithm, improved whale optimization algorithm, modifies the encircling phase of original whale optimization algorithm. The proposed algorithm has been used to cluster the extracted features for finding the relevant codewords to be used in the bag-of-features method for histopathological image classification. Results: The efficiency of proposed algorithm has been analyzed on 23 benchmark functions in terms of mean fitness, standard deviation values, and convergence behavior. The performance of the improved whale optimization algorithm based histopathological image classification method has been analyzed on blue histology image dataset and compared with other meta-heuristic based bagof- features methods in terms of recall, precision, F-measure, and accuracy. The experimental results validate that the proposed method outperforms the considered state-of-the-art methods and attains 12% increase in the histopathological image classification accuracy. Conclusion: In this paper, a new improved whale optimization algorithm has been proposed and applied in bag-of-features method for histopathological image classification. The results of proposed method outperform the other existing meta-heuristic methods over standard benchmark functions and histopathological image dataset.

scholarly journals Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

2019 ◽  
Vol 9 (18) ◽  
pp. 3755 ◽  
Author(s):  
Wei Chen ◽  
Haoyuan Hong ◽  
Mahdi Panahi ◽  
Himan Shahabi ◽  
Yi Wang ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Landslide Susceptibility ◽  
Heuristic Algorithms ◽  
Whale Optimization Algorithm ◽  
Economic Losses ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Prediction Ability ◽  
Inference System ◽  
Whale Optimization

The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.

Improved whale optimization algorithm based on random hopping update and random control parameter

2021 ◽  
Vol 40 (1) ◽  
pp. 363-379
Author(s):  
Yanju Guo ◽  
Huan Shen ◽  
Lei Chen ◽  
Yu Liu ◽  
Zhilong Kang
Keyword(s):  
Optimization Algorithm ◽  
Control Parameter ◽  
Heuristic Algorithms ◽  
Whale Optimization Algorithm ◽  
Original Algorithm ◽  
Multimodal Functions ◽  
Whale Optimization ◽  
Update Strategy ◽  
Random Control ◽  
Better Than

Whale Optimization Algorithm (WOA) is a relatively novel algorithm in the field of meta-heuristic algorithms. WOA can reveal an efficient performance compared with other well-established optimization algorithms, but there is still a problem of premature convergence and easy to fall into local optimal in complex multimodal functions, so this paper presents an improved WOA, and proposes the random hopping update strategy and random control parameter strategy to improve the exploration and exploitation ability of WOA. In this paper, 24 well-known benchmark functions are used to test the algorithm, including 10 unimodal functions and 14 multimodal functions. The experimental results show that the convergence accuracy of the proposed algorithm is better than that of the original algorithm on 21 functions, and better than that of the other 5 algorithms on 23 functions.

scholarly journals Loss Minimization by Reconfiguration in Radial Distribution Network using Whale Optimization Technique

2020 ◽  
Vol 9 (2) ◽  
pp. 1746-1751
Keyword(s):  
Radial Distribution ◽  
Optimization Algorithm ◽  
Distribution Systems ◽  
Heuristic Algorithms ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Whale Optimization Algorithm ◽  
Function Evaluation ◽  
Voltage Profile ◽  
Whale Optimization

This paper provides a new approach for solving the problem of network reconfiguration in the presence of Whale Optimization Algorithm (WOA). It is aimed at reducing actual power loss and enlightening the voltage profile in the supply system. The voltage and branch current capacity constraints have been included in the objective function evaluation. The method has been evaluated at three separate heuristic algorithms on 33-bus radial distribution systems to demonstrate the performance and effectiveness of the proposed method. In this paper the comparison of performance of two latest optimization techniques such as Whale Optimization Algorithm (WOA) with classic optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The new optimization technique produces better result compare to other two optimization logarithm..

scholarly journals Near Infrared and Visible Image Registration using Whale Optimization Algorithm

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Image Registration ◽  
Optimization Algorithm ◽  
Near Infrared ◽  
Optimization Problems ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Visible Image ◽  
Local Optima ◽  
Multimodal Image Registration ◽  
Whale Optimization

This paper reports the use of a nature-inspired metaheuristic algorithm known as ‘Whale Optimization Algorithm’ (WOA) for multimodal image registration. WOA is based on the hunting behaviour of Humpback whales and provides better exploration and exploitation of the search space with small possibility of trapping in local optima. Though WOA is used in various optimization problems, no detailed study is available for its use in image registration. For this study different sets of NIR and visible images are considered. The registration results are compared with the other state of the art image registration methods. The results show that WOA is a very competitive algorithm for NIR-visible image registration. With the advantages of better exploration of search space and local optima avoidance, the algorithm can be a suitable choice for multimodal image registration.

Sign in / Sign up

Export Citation Format

Share Document