Chaotic Flower Pollination and Grey Wolf Algorithms for parameter extraction of bio-impedance models

Yousri, Dalia; AbdelAty, Amr M.; Said, Lobna A.; Elwakil, Ahmed S.; Maundy, Brent; Radwan, Ahmed G.

doi:10.1016/j.asoc.2018.11.020

Cited by 55 publications

(30 citation statements)

References 48 publications

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“…The selection of the gradient descent method is based on its application in previous bioimpedance studies [15] and wide accessibility using MATLAB. Admittedly, there are other available optimization algorithms available such as Chaotic Flower Pollination [31] , Grew Wolf optimizer [31] , Moth-Flame optimizer [32] , Bacterial Foraging optimization [32] , and Particle Swarm Optimization [33] . The ease of accessibility and usability of implementing the GD technique makes it an appropriate choice for this work.…”

Section: Methodsmentioning

confidence: 99%

Cole-impedance parameters representing biceps tissue bioimpedance in healthy adults and their alterations following eccentric exercise

Freeborn

2020

Journal of Advanced Research

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Section: Methodsmentioning

confidence: 99%

Cole-impedance parameters representing biceps tissue bioimpedance in healthy adults and their alterations following eccentric exercise

Freeborn

2020

Journal of Advanced Research

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“…In a previous study 40 , chaotic maps have been considered to improve the performance of the whale optimization algorithm and balance the exploration and exploitation phases. Also, a grey wolf optimizer and flower pollination algorithm have been enhanced using ten chaotic maps to extract the parameters of the bio-impedance models 41 . Meanwhile, in 42 , the grasshopper optimization algorithm with chaos theory is employed to accelerate its global convergence and avoid local optimal.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics

Houssein

Hosney²,

Elhoseny

et al. 2020

Sci Rep

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one of the major drawbacks of cheminformatics is a large amount of information present in the datasets. In the majority of cases, this information contains redundant instances that affect the analysis of similarity measurements with respect to drug design and discovery. therefore, using classical methods such as the protein bank database and quantum mechanical calculations are insufficient owing to the dimensionality of search spaces. In this paper, we introduce a hybrid metaheuristic algorithm called cHHo-cS, which combines Harris hawks optimizer (HHo) with two operators: cuckoo search (cS) and chaotic maps. the role of cS is to control the main position vectors of the HHo algorithm to maintain the balance between exploitation and exploration phases, while the chaotic maps are used to update the control energy parameters to avoid falling into local optimum and premature convergence. feature selection (fS) is a tool that permits to reduce the dimensionality of the dataset by removing redundant and non desired information, then fS is very helpful in cheminformatics. FS methods employ a classifier that permits to identify the best subset of features. the support vector machines (SVMs) are then used by the proposed cHHo-cS as an objective function for the classification process in FS. The CHHO-CS-SVM is tested in the selection of appropriate chemical descriptors and compound activities. Various datasets are used to validate the efficiency of the proposed CHHO-CS-SVM approach including ten from the UCI machine learning repository. Additionally, two chemical datasets (i.e., quantitative structure-activity relation biodegradation and monoamine oxidase) were utilized for selecting the most significant chemical descriptors and chemical compounds activities. the extensive experimental and statistical analyses exhibit that the suggested CHHO-CS method accomplished much-preferred trade-off solutions over the competitor algorithms including the HHO, CS, particle swarm optimization, moth-flame optimization, grey wolf optimizer, Salp swarm algorithm, and sine-cosine algorithm surfaced in the literature. the experimental results proved that the complexity associated with cheminformatics can be handled using chaotic maps and hybridizing the meta-heuristic methods. The prediction and analysis of molecules are essential tasks in cheminformatics, which use methods from mathematics and computer science to enhance their performance. The implementation of these methods depends on databases. The processes that generate most of the affectations are the storage and retrieval of molecular structures and properties (e.g., pharmacogenomics data). Typically, the behavior of the compounds can be investigated using molecular analysis. The molecular analysis helps to develop and test molecules for decreasing the effects of specific diseases 1. One drawback associated with cheminformatics is the exponential increment of the search space owing to features in the dataset 2. However, cheminformatics is still being widely used in drug design, where ...

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“…Chaos is a deterministic, pseudo-random, non-converging, non-period and bounded method can be found in non-linear dynamical systems [51]. Several attempts were made to prove that the randomness and the dynamical properties of the chaos maps help the optimization techniques to overcome from local optima [52][53][54]. Therefore, in this paper, by recognizing the importance of chaotic maps in improving the accuracy, consistency and convergence speed of the basic algorithms, the authors are motivated to employ a new version of these maps named fractional chaotic maps as the first time in MPPT application.…”

Section: Fractional Chaotic Flower Pollination Algorithm Variants (Fcmentioning

confidence: 99%

Fractional Chaos Maps with Flower Pollination Algorithm for Partial Shading Mitigation of Photovoltaic Systems

et al. 2019

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Solar Photovoltaic (PV) systems have become prominent and have attained the attention of energy engineers, governments and researchers. To achieve the maximum benefit from the PV system in spite of its nonlinear characteristic and environmental conditions, finding a robust maximum power point tracking method is essential. Over two decades, various researchers proposed numerous MPPT methods, but they failed to evaluate their methods on consistency, reliability, and robustness over several numbers of runs. Most of the researchers examined one configuration and they did not to consider the dynamic change in the irradiation conditions. Therefore, in this manuscript, the authors introduced a novel optimization technique Fractional chaotic Flower Pollination Algorithm (FC-FPA), by merging fractional chaos maps with flower pollination algorithm (FPA). The proposed technique, help FPA in extracting the Global Maximum Power Point (GMPP) under different partial shading patterns including with different PV array configurations. The proposed FC-FPA technique is tested and evaluated over 5 different patterns of partial shading conditions. The first three patterns are tested over 4S configuration made with Shell S36 PV module. The other two patterns are applied to the 4S2P configuration of Shell SM55 PV panels. The performance of the proposed variant is investigated by tracking the GMPP for abruptly changing shade pattern. Exclusive statistical analysis is performed over several numbers of runs. Comparison with perturb and observe MPPT technique is established. These results confirm that, the proposed method shows fast convergence, zero oscillation and rapid response for the dynamic change in irradiation with consistent behavior.

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Chaotic Flower Pollination and Grey Wolf Algorithms for parameter extraction of bio-impedance models

Cited by 55 publications

References 48 publications

Cole-impedance parameters representing biceps tissue bioimpedance in healthy adults and their alterations following eccentric exercise

Cole-impedance parameters representing biceps tissue bioimpedance in healthy adults and their alterations following eccentric exercise

Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics

Fractional Chaos Maps with Flower Pollination Algorithm for Partial Shading Mitigation of Photovoltaic Systems

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