Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

Li, Linguo; Sun, Lijuan; Guo, Jian; Qi, Jin; Bin, Xu; Li, Shujing

doi:10.1155/2017/3295769

Cited by 51 publications

(33 citation statements)

References 39 publications

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“…Further down the pyramid is the Delta which must follow the orders of the alpha and beta wolves but has domination over the omega. The duty of the delta is to defend and provide safety to all family members [75]. Omega is the lowest ranked among the grey wolf family and plays the role of scapegoat.…”

Section: Grey Wolf Optimizer Methodsmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and "black-box" forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO). These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods.

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Section: Grey Wolf Optimizer Methodsmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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“…It has an efficient updating mechanism. Though, this mechanism can be improved and enhanced [31]. Initializing grey wolves' population is randomized in order to diversify the population.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design

Mohammed

Rashid

2020

Neural Comput & Applic

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A recent metaheuristic algorithm, such as Whale Optimization Algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey Wolf Optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and Fitness Dependent Optimizer (FDO).

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“…e median filter, the four edge detection algorithms, and the employed morphological operation all necessitate appropriate settings of their tuning parameters. As being observed in the literature, the selection of parameters of image processing models can be formulated as optimization problems [22][23][24]. Hence, our study relies on the differential flower pollination, as a metaheuristic algorithm, to optimize the crack detection model by means of identifying an appropriate set of model hyperparameters.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Optimized Edge Detection for Recognition of Concrete Wall Cracks: A Comparative Study on the Performances of Roberts, Prewitt, Canny, and Sobel Algorithms

Hoang

Nguyen

2018

Advances in Civil Engineering

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Crack detection is a crucial task in the periodic survey of high-rise buildings and infrastructure. Manual survey is notorious for low productivity. is study is aimed at establishing an image processing-based method for detecting cracks on concrete wall surfaces in an automatic manner. e Roberts, Prewitt, Canny, and Sobel algorithms are employed as the edge detection methods for revealing the crack textures appearing in concrete walls. e median filtering and object cleaning operations are used to enhance the image and facilitate the crack recognition outcome. Since the edge detectors, the median filter, and the object cleaning operation all require the appropriate selection of tuning parameters, this study relies on the differential flower pollination algorithm as a metaheuristic to optimize the image processing-based crack detection model. Experimental results point out that the newly constructed approach that employs the Prewitt algorithm can achieve a good prediction outcome with classification accuracy rate � 89.95% and area under the curve � 0.90. erefore, the proposed metaheuristic optimized image processing approach can be a promising alternative for automatic recognition of cracks on the concrete wall surface.

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Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

Cited by 51 publications

References 39 publications

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design

Metaheuristic Optimized Edge Detection for Recognition of Concrete Wall Cracks: A Comparative Study on the Performances of Roberts, Prewitt, Canny, and Sobel Algorithms

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