Liver segmentation in MRI images based on whale optimization algorithm

Mostafa, Abdalla; Hassanien, Aboul Ella; Houseni, Mohamed; Hefny, Hesham A.

doi:10.1007/s11042-017-4638-5

Cited by 89 publications

(44 citation statements)

References 24 publications

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“…The load profile data used to support the findings of this study are included within the article. Other data such as line data and bus data have been explained in References [20,21].…”

Section: Abbreviationsmentioning

confidence: 99%

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Multiple DGs for Reducing Total Power Losses in Radial Distribution Systems Using Hybrid WOA-SSA Algorithm

Alzaidi

Bayat

Uçan

2019

International Journal of Photoenergy

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Distributed generators (DGs) are currently extensively used to reduce power losses and voltage deviations in distribution networks. The optimal location and size of DGs achieve the best results. This study presents a novel hybridization of new metaheuristic optimizations in the last two years, namely, salp swarm algorithm (SSA) and whale optimization algorithm (WOA), for optimal placement and size of multi-DG units in radial distribution systems to minimize total real power losses (kW) and solve voltage deviation. This hybrid algorithm is implemented on IEEE 13- and 123-node radial distribution test systems. The OpenDSS engine is used to solve the power flow to find the power system parameters, such power losses, and the voltage profile through the MATLAB coding interface. Results describe the effectiveness of the proposed hybrid WOA-SSA algorithm compared with those of the IEEE standard case (without DG), repeated load flow method, and WOA and SSA algorithms applied independently. The analysis results via the proposed algorithm are more effective for reducing total active power losses and enhancing the voltage profile for various distribution networks and multi-DG units.

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“…The load profile data used to support the findings of this study are included within the article. Other data such as line data and bus data have been explained in References [20,21].…”

Section: Abbreviationsmentioning

confidence: 99%

“…Additionally, it is used by many researchers in different optimization areas. Mostafa et al [21] proposed an approach for liver segmentation in MRI images based on WOA. Sayed et al [22] proposed a novel optimization algorithm called chaotic whale optimization algorithm (CWOA) for feature selection based on the chaos theory and WOA.…”

mentioning

confidence: 99%

Multiple DGs for Reducing Total Power Losses in Radial Distribution Systems Using Hybrid WOA-SSA Algorithm

Alzaidi

Bayat

Uçan

2019

International Journal of Photoenergy

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“…The simulation results show that the proposed algorithm has better performance than other swarm intelligence algorithms. The literature [27] propose a MRI image liver segmentation method based on the whale optimization algorithm (WOA). The proposed method uses a set of 70 magnetic resonance images (MRI) images for testing.…”

Section: Introductionmentioning

confidence: 99%

An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance

2018

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Whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm inspired by humpback whale hunting behavior. WOA has many similarities with other swarm intelligence algorithms (PSO, GWO, etc.). WOA's unique search mechanism enables it to have a strong global search capability while taking into account the strong global search capabilities. In this work, considering the the deficiency of WOA in local search mechanism, combined with the optimization methods of other group intelligent algorithms, perceptual perturbation mechanism is introduced, which makes the agent perform more detailed searches near the local extreme point. At the same time, since the WOA uses a logarithmic spiral curve, the agent cannot fully search all the spaces within its search range, even though the introduction of the perturbation mechanism may still lead to the algorithm falling into a local optimum. Therefore, the equal pitch Archimedes spiral curve is chosen to replace the classic logarithmic spiral curve. In order to fully verify the effect of the search path on the performance of the algorithm, several other spiral curves have been chosen for experimental comparison. By utilizing the 23 benchmark test functions, the simulation results show that WOA (PDWOA) with perceptual perturbation significantly outperforms the standard WOA. Then, based on the PDWOA, the effect of the search path on the performance of the algorithm has been verified. The simulation results show that the equal pitch of the Archimedean spiral curve is best.

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“…Aziz et al applied WOA to multilevel thresholding image segmentation. 13 Aljarah et al used WOA for training multilayer perceptron (MLP) neural networks. 12 Mostafa et al employed WOA in the medical field segmentation, more specifically for liver segmentation in MRI images.…”

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confidence: 99%

“…12 Mostafa et al employed WOA in the medical field segmentation, more specifically for liver segmentation in MRI images. 13 Aljarah et al used WOA for training multilayer perceptron (MLP) neural networks. 14 Mafarja and Mirjalili proposed a hybrid WOA by integrating it with SA for feature selection.…”

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confidence: 99%

Distributed Whale Optimization Algorithm based on MapReduce

Khalil

Alshayeji

Ahmad

2018

Concurrency and Computation

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Whale Optimization Algorithm (WOA) is a recent swarm intelligence based meta-heuristic optimization algorithm, which simulates the natural behavior of bubble-net hunting strategy of humpback whales and has been successfully applied to solve complex optimization problems in a wide range of disciplines. However, when applied to large-size problems, its performance degrades due to the need of massive computational work load. Distributed computing is one of the effective ways to improve the scalability of WOA for solving large-scale problems. In this paper, we propose a simple and robust distributed implementation of WOA using Hadoop MapReduce named MR-WOA. MapReduce paradigm is adopted as the distribution model since it is one of the most mature technologies to develop parallel algorithms which automatically handles communication, load balancing, data locality, and fault tolerance. The design of MR-WOA is discussed in details using the MapReduce paradigm. Experiments are conducted for a set of well-known benchmarks for evaluating the quality, speedup, and scalability of MR-WOA. The conducted experiments reveal that our approach achieves a promising speedup. For some benchmarks, speedup scales linearly with increasing the number of computational nodes.

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Liver segmentation in MRI images based on whale optimization algorithm

Cited by 89 publications

References 24 publications

Multiple DGs for Reducing Total Power Losses in Radial Distribution Systems Using Hybrid WOA-SSA Algorithm

Multiple DGs for Reducing Total Power Losses in Radial Distribution Systems Using Hybrid WOA-SSA Algorithm

An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance

Distributed Whale Optimization Algorithm based on MapReduce

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