scholarly journals Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Linguo Li ◽  
Lijuan Sun ◽  
Jian Guo ◽  
Jin Qi ◽  
Bin Xu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Optimal Solution ◽  
Weight Coefficient ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Original Algorithm ◽  
Image Thresholding ◽  
Bee Colony ◽  
Optimal Thresholds ◽  
Objective Function Values

The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur’s entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability.

Thresholding Selection Based on Fuzzy Entropy and Bee Colony Algorithm for Image Segmentation

2013 ◽  
pp. 165-183
Author(s):  
Yonghao Xiao ◽  
Weiyu Yu ◽  
Jing Tian
Keyword(s):  
Image Segmentation ◽  
Fuzzy Entropy ◽  
Single Parameter ◽  
Entropy Function ◽  
Experimental Results ◽  
Superior Performance ◽  
Image Thresholding ◽  
Minimum Function ◽  
Minimum Value ◽  
Bee Colony

Image thresholding segmentation based on Bee Colony Algorithm (BCA) and fuzzy entropy is presented in this chapter. The fuzzy entropy function is simplified with single parameter. The BCA is applied to search the minimum value of the fuzzy entropy function. According to the minimum function value, the optimal image threshold is obtained. Experimental results are provided to demonstrate the superior performance of the proposed approach.

Elephant Herding Optimization for Multi-Level Image Thresholding

2020 ◽  
Vol 11 (4) ◽  
pp. 64-90
Author(s):  
Falguni Chakraborty ◽  
Provas Kumar Roy ◽  
Debashis Nandi
Keyword(s):  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Cuckoo Search ◽  
Threshold Value ◽  
Objective Functions ◽  
Image Thresholding ◽  
Bee Colony ◽  
Kapur’S Entropy ◽  
Feature Similarity Index

Multilevel thresholding plays a significant role in the arena of image segmentation. The main issue of multilevel image thresholding is to select the optimal combination of threshold value at different level. However, this problem has become challenging with the higher number of levels, because computational complexity is increased exponentially as the increase of number of threshold. To address this problem, this paper has proposed elephant herding optimization (EHO) based multilevel image thresholding technique for image segmentation. The EHO method has been inspired by the herding behaviour of elephant group in nature. Two well-known objective functions such as ‘Kapur's entropy' and ‘between-class variance method' have been used to determine the optimized threshold values for segmentation of different objects from an image. The performance of the proposed algorithm has been verified using a set of different test images taken from a well-known benchmark dataset named Berkeley Segmentation Dataset (BSDS). For comparative analysis, the results have been compared with three popular algorithms, e.g. cuckoo search (CS), artificial bee colony (ABC) and particle swarm optimization (PSO). It has been observed that the performance of the proposed EHO based image segmentation technique is efficient and promising with respect to the others in terms of the values of optimized thresholds, objective functions, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The algorithm also shows better convergence profile than the other methods discussed.

scholarly journals An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1457
Author(s):  
Avelina Alejo-Reyes ◽  
Erik Cuevas ◽  
Alma Rodríguez ◽  
Abraham Mendoza ◽  
Elias Olivares-Benitez
Keyword(s):  
Supplier Selection ◽  
Search Strategy ◽  
Optimization Problems ◽  
Displacement Vector ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Order Quantity ◽  
Hunting Behavior

Supplier selection and order quantity allocation have a strong influence on a company’s profitability and the total cost of finished products. From an optimization perspective, the processes of selecting the right suppliers and allocating orders are modeled through a cost function that considers different elements, such as the price of raw materials, ordering costs, and holding costs. Obtaining the optimal solution for these models represents a complex problem due to their discontinuity, non-linearity, and high multi-modality. Under such conditions, it is not possible to use classical optimization methods. On the other hand, metaheuristic schemes have been extensively employed as alternative optimization techniques to solve difficult problems. Among the metaheuristic computation algorithms, the Grey Wolf Optimization (GWO) algorithm corresponds to a relatively new technique based on the hunting behavior of wolves. Even though GWO allows obtaining satisfying results, its limited exploration reduces its performance significantly when it faces high multi-modal and discontinuous cost functions. In this paper, a modified version of the GWO scheme is introduced to solve the complex optimization problems of supplier selection and order quantity allocation. The improved GWO method called iGWO includes weighted factors and a displacement vector to promote the exploration of the search strategy, avoiding the use of unfeasible solutions. In order to evaluate its performance, the proposed algorithm has been tested on a number of instances of a difficult problem found in the literature. The results show that the proposed algorithm not only obtains the optimal cost solutions, but also maintains a better search strategy, finding feasible solutions in all instances.

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

2020 ◽  
Vol 10 (18) ◽  
pp. 6343
Author(s):  
Yuanyuan Liu ◽  
Jiahui Sun ◽  
Haiye Yu ◽  
Yueyong Wang ◽  
Xiaokang Zhou
Keyword(s):  
Differential Evolution ◽  
Image Recognition ◽  
Optimal Solution ◽  
Tsallis Entropy ◽  
Grey Wolf Optimizer ◽  
Initial Population ◽  
Grey Wolf ◽  
Particle Swarm Optimizer ◽  
De Algorithm ◽  
Local Optimal Solution

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.

Medical Image Segmentation using PCNN based on Multi-feature Grey Wolf Optimizer Bionic Algorithm

2021 ◽  
Vol 18 (3) ◽  
pp. 711-720
Author(s):  
Xue Wang ◽  
Zhanshan Li ◽  
Heng Kang ◽  
Yongping Huang ◽  
Di Gai
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Grey Wolf Optimizer ◽  
Grey Wolf

Color Image Segmentation Based on Self-Organizing Maps

2011 ◽  
Vol 214 ◽  
pp. 693-698
Author(s):  
Rui Geng
Keyword(s):  
Image Segmentation ◽  
Optimization Problems ◽  
Color Image ◽  
Optimal Solution ◽  
Implementation Process ◽  
Segmentation Algorithm ◽  
Color Image Segmentation ◽  
Combinatorial Optimization Problems ◽  
Bee Colony ◽  
Image Segmentation Algorithm

The colony intellectual behavior performed by many organisms in nature can solve various kinds of problems on scientific and technological research. Bees are a socialized insect colony, which perform different types of activities according to their different divisions of labor, and achieve information sharing and exchanges among the bee colony to find the optimal solution for problems. According to this characteristic, researchers have proposed the algorithm of bee colony for solving combinatorial optimization problems. In this paper, it will describe the implementation process of such an image segmentation algorithm, and the result shows that this method is a potential image segmentation algorithm.

scholarly journals Optimal Power Flow Solutions Incorporating Stochastic Solar Power withthe Application Grey Wolf Optimize

2021 ◽  
Vol 03 (01) ◽  
pp. 74-84
Author(s):  
Nadir Taleb ◽  
◽  
Bachir Bentouati ◽  
Saliha Chettih ◽  
◽  
...  
Keyword(s):  
Renewable Energy ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Optimal Solution ◽  
Electrical Network ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Renewable Energy Resources ◽  
Network Constraints ◽  
Optimal Power

The present paper aims to validate an electrical network study in consisting of conventional fossil fuel generators with the integration of intermittent generation technologies based on renewable energy resources like wind power or solar photovoltaic (PV) are the stochastic power output. By using an optimal power flow (OPF) problem different frameworks are developed for solving that represent various operating requirements, such as minimization of production fuel cost, and preserving generation emission at the lowest levels... etc. The OPF analysis aims to find the optimal solution and is very important for power system operation with satisfying operational constraints, planning and energy management. However, the intermittent combination of solar exacerbates the complexity of the problem. Within the framework of these criteria, this paper is an overview of the application Grey Wolf Optimizer (GWO) algorithm which solves the OPF problem with renewable energy. The algorithm thus combined and constructed gives optimum results satisfying all network constraints. Give an explanation for findings are based thus need to be with the optimum to effectuate of network constraints.

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