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

Alwerfali, Husein S. Naji; Al-qaness, Mohammed A. A.; Elaziz, Mohamed E. Abd; Ewees, Ahmed A.; Oliva, Diego; Lu, Songfeng

doi:10.3390/e22030328

Cited by 35 publications

(13 citation statements)

References 48 publications

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“…Traditional optimization methods have several drawbacks when solving complex and complicated problems that require considerable time and cost optimization. Metaheuristic algorithms have been proven capable of handling a variety of continuous and discrete optimization problems [46] in a wide range of applications including engineering [47][48][49], industry [50,51], image processing and segmentation [52][53][54], scheduling [55,56], photovoltaic modeling [57,58], optimal power flow [59,60], power and energy management [61,62], planning and routing problems [63][64][65], intrusion detection [66,67], feature selection [68][69][70][71][72], spam detection [73,74], medical diagnosis [75][76][77], quality monitoring [78], community detection [79], and global optimization [80][81][82]. In the following, some representative metaheuristic algorithms from the swarm intelligence category used in our experiments are described.…”

Section: Related Workmentioning

confidence: 99%

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

et al. 2021

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The optimal power flow (OPF) is a vital tool for optimizing the control parameters of a power system by considering the desired objective functions subject to system constraints. Metaheuristic algorithms have been proven to be well-suited for solving complex optimization problems. The whale optimization algorithm (WOA) is one of the well-regarded metaheuristics that is widely used to solve different optimization problems. Despite the use of WOA in different fields of application as OPF, its effectiveness is decreased as the dimension size of the test system is increased. Therefore, in this paper, an effective whale optimization algorithm for solving optimal power flow problems (EWOA-OPF) is proposed. The main goal of this enhancement is to improve the exploration ability and maintain a proper balance between the exploration and exploitation of the canonical WOA. In the proposed algorithm, the movement strategy of whales is enhanced by introducing two new movement strategies: (1) encircling the prey using Levy motion and (2) searching for prey using Brownian motion that cooperate with canonical bubble-net attacking. To validate the proposed EWOA-OPF algorithm, a comparison among six well-known optimization algorithms is established to solve the OPF problem. All algorithms are used to optimize single- and multi-objective functions of the OPF under the system constraints. Standard IEEE 6-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems are used to evaluate the proposed EWOA-OPF and comparative algorithms for solving the OPF problem in diverse power system scale sizes. The comparison of results proves that the EWOA-OPF is able to solve single- and multi-objective OPF problems with better solutions than other comparative algorithms.

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Section: Related Workmentioning

confidence: 99%

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

et al. 2021

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“…In the same context of MLT segmentation image based on hybrid metaheuristics, a new combination between Spherical search optimizer (SSO) and sine cosine algorithm (SCA) is developed by Husein et al [33]. The fuzzy entropy is considered as the main fitness function for testing the quality of the segmented image.…”

Section: Related Workmentioning

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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“…Generally, meta-heuristic algorithms are introduced by mimicking natural behaviors of groups in the environment, such as wild animals, flying animals, science, theories, and others. In [16], a new multilevel thresholding approach was presented based on a new version of the spherical search optimization (SSO) algorithms, called SSOSCA. The proposed method used operators from the components of the conventional SCA to improve the exploitation searchability of the conventional SSO algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation

et al. 2021

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Multilevel thresholding is one of the most effective image segmentation methods, due to its efficiency and easy implementation. This study presents a new multilevel thresholding method based on a modified artificial ecosystem-based optimization (AEO). The differential evolution (DE) is applied to overcome the shortcomings of the original AEO. The main idea of the proposed method, artificial ecosystem-based optimization differential evolution (AEODE), is to employ the operators of the DE as a local search of the AEO to improve the ecosystem of solutions. We used benchmark images to test the performance of the AEODE, and we compared it to several existing approaches. The proposed AEODE achieved a high performance when evaluated by the structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and fitness values. Moreover, the AEODE outperformed the basic version of the AEO concerning SSIM and PSNR by 78% and 82%, respectively, which reserves the best features for each of AEO and DE.

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

Cited by 35 publications

References 48 publications

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

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

Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation

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