Voltage Profile Improvement in Active Distribution Networks Using Hybrid WOA-SCA Optimization Algorithm

Selim, Ali; Kamel, Salah

doi:10.1109/mepcon.2018.8635213

Cited by 27 publications

(5 citation statements)

References 16 publications

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“…WOA-SCA was proposed by Selim et al (2018) for voltage profile improvement in active distribution networks. WOA was combined with SCA to raise its convergence.…”

Section: Hybridization With Whale Optimization Algorithmmentioning

confidence: 99%

A comprehensive survey of sine cosine algorithm: variants and applications

et al. 2021

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Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration rate, and high efficiency compared to several well-regarded optimization algorithms available in the literature. This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions. Furthermore, the applications of SCA in several domains such as classification, image processing, robot path planning, scheduling, radial distribution networks, and other engineering problems are described. Finally, the paper recommended some potential future research directions for SCA.

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“…WOA-SCA was proposed by Selim et al (2018) for voltage profile improvement in active distribution networks. WOA was combined with SCA to raise its convergence.…”

Section: Hybridization With Whale Optimization Algorithmmentioning

confidence: 99%

A comprehensive survey of sine cosine algorithm: variants and applications

et al. 2021

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“…Kaveh et al utilized the hybrid WOA-CBO to solve the problem of layout planning, colliding bodies optimization are mixed into WOA, and the result shows that it had better advantages compared with other algorithms [23]. Selim et al proposed a hybrid WOA with sine cosine (SC) [24]. e results testified the effectiveness and the proposed algorithm optimized the voltage profile in the system.…”

Section: Introductionmentioning

confidence: 99%

Seismic Inversion Problem Using a Multioperator Whale Optimization Algorithm

Liang

2022

Discrete Dynamics in Nature and Society

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The whale optimization algorithm (WOA) is a metaheuristic algorithm based on swarm intelligence and it mimics the hunting behavior of whales. It has the imperfection of premature convergence into local optima. In order to overcome this disadvantage, a multioperator WOA (MOWOA) is proposed. Four main strategies are introduced to the MOWOA to heighten the search capacity of WOA. The strategies include nonlinear adaptive parameter design, an exploration mechanism of honey badger, Cauchy factor strategy, and greedy strategy. This paper tests the versatility of MOWOA with three different types of benchmark functions, and a kind of seismic inversion problem are trialed run. From the experimental results, the performance of MOWOA outperforms the compared algorithms in global optimization.

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“…Hra et al [24] constructed a hybrid WOA-PSO algorithm for optimally integrated river basin management. Selim et al [25] used the combination of the sine cosine algorithm and WOA for voltage profile improvement in active distribution networks. The second is to optimize WOA using existing multiple strategies.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Pan

Xia

et al. 2021

Symmetry

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The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

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Voltage Profile Improvement in Active Distribution Networks Using Hybrid WOA-SCA Optimization Algorithm

Cited by 27 publications

References 16 publications

A comprehensive survey of sine cosine algorithm: variants and applications

A comprehensive survey of sine cosine algorithm: variants and applications

Seismic Inversion Problem Using a Multioperator Whale Optimization Algorithm

A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

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