Improved Artificial Bee Colony Using Sine-Cosine Algorithm for Multi-Level Thresholding Image Segmentation

Ewees, Ahmed A.; Elaziz, Mohamed E. Abd; Al-qaness, Mohammed A. A.; Khalil, Hassan A.; Kim, Sunghwan

doi:10.1109/access.2020.2971249

Cited by 79 publications

(41 citation statements)

References 70 publications

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“…For ML-TH, a new class of metaheuristic algorithms based on genetic evolution, swarm theory, and physical laws have been applied. Several methods, such as genetic algorithm (GA) [45], differential evolution (DE) [41], particle swarm optimization (PSO) [2], multi-verse optimizer (MVO) [11], artificial bee colony (ABC) [14], artificial bee colony (ABC) [18], chicken swarm optimization [28], electromagnetism optimization [34], and gravitational search algorithm (GSA) [31], are available in the literature. They are applied to obtain the optimal set of thresholding by maximizing the interclass variance defined by Otsu's function.…”

Section: Introductionmentioning

confidence: 99%

Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm

Elaziz

Neggaz

Moghdani

et al. 2021

Multimed Tools Appl

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Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index.

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Section: Introductionmentioning

confidence: 99%

Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm

Elaziz

Neggaz

Moghdani

et al. 2021

Multimed Tools Appl

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“…as, thresholding of images [6], classification of satellite images [7] as well as optimizing benchmark functions [8], etc. Metaheuristics also have an excellent exploitation capability and local optima avoidance mechanism, thus making them a popular choice for solving optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Border Collie Optimization

Dutta

Bhattacharyya

Dey³

et al. 2020

IEEE Access

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In recent times, several metaheuristic algorithms have been proposed for solving real world optimization problems. In this paper, a new metaheuristic algorithm, called the Border Collie Optimization is introduced. The algorithm is developed by mimicking the sheep herding styles of Border Collie dogs. The Border Collie's unique herding style from the front as well as from the sides is adopted successfully in this paper. In this algorithm, the entire population is divided into two parts viz., dogs and sheep. This is done to equally focus on both exploration and exploitation of the search space. The Border Collie utilizes a predatory move called eyeing. This technique of the dogs is utilized to prevent the algorithm from getting stuck into local optima. A sensitivity analysis of the proposed algorithm has been carried out using the Sobol's sensitivity indices with the Sobol g-function for tuning of parameters. The proposed algorithm is applied on thirty-five benchmark functions. The proposed algorithm provides very competitive results, when compared with seven state-of-the-art algorithms like Ant Colony optimization, Differential algorithm, Genetic algorithm, Grey-wolf optimizer, Harris Hawk optimization, Particle Swarm optimization and Whale optimization algorithm. The performance of the proposed algorithm is analytically and visually tested by different methods to judge its supremacy. Finally, the statistical significance of the proposed algorithm is established by comparing it with other algorithms by employing Kruskal-Wallis test and Friedman test.

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“…In Reference [ 31 ], the authors applied SCA to enhance simulated annealing (SA) algorithm to build an efficient model for scheduling jobs in unrelated parallel machines that can be employed in manufacturing scheduling applications. In Reference [ 32 ], the SCA is applied to enhance the artificial bee colony (ABC) that applied for image segmentation. It is used to update individual solutions to find the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy

Alwerfali

Al-qaness

Elaziz

et al. 2020

Entropy

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Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been proposed by simulating natural behaviors of swarm ecosystems, such as birds, animals, and others. The current study proposes an alternative multi-level thresholding method based on a new MH method, a modified spherical search optimizer (SSO). This was performed by using the operators of the sine cosine algorithm (SCA) to enhance the exploitation ability of the SSO. Moreover, Fuzzy entropy is applied as the main fitness function to evaluate the quality of each solution inside the population of the proposed SSOSCA since Fuzzy entropy has established its performance in literature. Several images from the well-known Berkeley dataset were used to test and evaluate the proposed method. The evaluation outcomes approved that SSOSCA showed better performance than several existing methods according to different image segmentation measures.

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Improved Artificial Bee Colony Using Sine-Cosine Algorithm for Multi-Level Thresholding Image Segmentation

Cited by 79 publications

References 70 publications

Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm

Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm

Border Collie Optimization

Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy

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