Henry gas solubility optimization: A novel physics-based algorithm

Hashim, Fatma A.; Houssein, Essam H.; Mabrouk, Mai S.; Al-Atabany, Walid; Mirjalili, Seyedali

doi:10.1016/j.future.2019.07.015

Cited by 726 publications

(246 citation statements)

References 74 publications

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“…In this subsection, the results of FO-MPA are compared against most popular and recent feature selection algorithms, such as Whale Optimization Algorithm (WOA) 49 , Henry Gas Solubility optimization (HGSO) 50 , Sine cosine Algorithm (SCA), Slime Mould Algorithm (SMA) 51 , Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) 52 , Harris Hawks Optimization (HHO) 53 , Genetic Algorithm (GA), and basic MPA. In this paper, each feature selection algorithm were exposed to select the produced feature vector from Inception aiming at selecting only the most relevant features.…”

Section: Resultsmentioning

confidence: 99%

COVID-19 image classification using deep features and fractional-order marine predators algorithm

Sahlol

Yousri

Ewees

et al. 2020

Sci Rep

196

136

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Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-19, which causes dangerous symptoms to humans and animals, its complications may lead to death. Although convolutional neural networks (CNNs) is considered the current state-of-the-art image classification technique, it needs massive computational cost for deployment and training. In this paper, we propose an improved hybrid classification approach for COVID-19 images by combining the strengths of CNNs (using a powerful architecture called Inception) to extract features and a swarm-based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. A combination of fractional-order and marine predators algorithm (FO-MPA) is considered an integration among a robust tool in mathematics named fractional-order calculus (FO). The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. By achieving 98.7%, 98.2% and 99.6%, 99% of classification accuracy and F-Score for dataset 1 and dataset 2, respectively, the proposed approach outperforms several CNNs and all recent works on COVID-19 images.

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Section: Resultsmentioning

confidence: 99%

COVID-19 image classification using deep features and fractional-order marine predators algorithm

Sahlol

Yousri

Ewees

et al. 2020

Sci Rep

196

136

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“…Fatma et al [19] introduced a novel metaheuristic algorithm in 2019, inspired from a well-known Henry's law of physics related to gas solubility, hence called Henry Gas Solubility Optimization (HGSO) algorithm. Based on extensive experimental analysis, the research proved that HGSO not only outperformed other recent and established metaheuristic algorithms, but also maintained outstanding ability of finding global optimum locations.…”

Section: Henry Gas Solubility Optimization (Hgso)mentioning

confidence: 99%

“…According to authors, the proposed HGSO maintains significant balance between exploration and exploitation abilities. A greater detail related to Henry's law and HGSO inspiration can be found in original work in [19]; however, a precise description is given in this section.…”

Section: Henry Gas Solubility Optimization (Hgso)mentioning

confidence: 99%

“…Keeping in view the aforementioned issues in standard ANFIS, this current study modifies its architecture for enhanced prediction accuracy and less computational complexity. The study employs recently introduced metaheuristic algorithm, known as Henry Gas Solubility Optimization (HGSO) [19], for updating adjustable parameters in ANFIS network. Moreover, the large rule-base in architecture is optimized by selecting only the useful rules that have effective contribution in enhancing model accuracy.…”

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A Novel Metaheuristic Approach to Optimization of Neuro-Fuzzy System for Students' Performance Prediction

Hussain¹,

Talpur²,

Aftab³

et al. 2020

JSCDM

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Today, technology is aggressively adopted by educational institutes to maximize output of existing resources, to benefit both students and the teachers. The use of technology-based learning makes it possible to collect enormous amount of data pertaining to student and teacher performance. This data is then used for extracting meaningful information that deems useful for administration in making efficient decisions. For this purpose, literature shows significant advantages of data mining and machine learning techniques with regards to educational data mining [1,2,3]. Utilizing behavioral and performance data of students and teachers, researchers have applied these methods to discover knowledge in hidden patterns, to assess the effectiveness of courses, teaching methodologies, curriculum, and overall education system [4]. More specifically, the educational data mining is now being used to predict students' performances in order to identify the vulnerable ones at risk of failing the upcoming examinations so that more focus could be drawn on them to solve any difficulties [5,6].

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“…Eğim (alçalma/yükselme) açısı, bir uçağın kalkışında ve inişinde önemli bir rol oynar. Son yıllarda, genetik algoritma [9], parçacık sürü optimizasyonu [2], [10], arı algoritması [11], yarasa algoritması [12] ve bakteriyel yemleme optimizasyonu [2] [13]. Söz konusu bu algoritma aynı zamanda motif keşif problemi [14], otomobil fren bileşenlerinin optimum yapısal tasarımı [15] ve özellik seçimi [16] için de kullanılmıştır.…”

Section: Introductionunclassified

Henry Gaz Çözünürlük Optimizasyonu ile Uçak Eğim Kontrol Sistemi için Etkin Kontrolör Tasarımı

Kaçti¹,

Ekinci²,

İzci³

2020

DÜMF Mühendislik Dergisi

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Sezgisel-üstü algoritmalar, uçak eğim kontrol sistemi, PID kontrolör ÖZET Bu çalışma ile sezgisel-üstü algoritmalardan olan Henry gaz çözünürlük optimizasyonu (HGSO) kullanılarak oransal, integral ve türevsel (PID) bir kontrolöre ait parametreler bir uçağın alçalma/yükselme (eğim) açısına ait sistemin kontrolü için optimum olacak şekilde ayarlanmıştır. Kullanılan bu yaklaşım literatürde ilk defa önerilmekte olup, sistemin performans analizi için istatistiksel test, geçici hal cevabı, kutup-sıfır haritası ve bode gibi analizler gerçekleştirilmiştir. Söz konusu bu analizler aynı zamanda literatürde son beş yıl içinde yayınlanmış ve oldukça etkili olduğu gösterilmiş olan sinüs kosinüs algoritması (SCA) ve çekirge optimizasyon algoritması (GOA) gibi diğer sezgisel-üstü algoritmalar ile ayarlanmış PID kontrolörler ile de kıyaslanmıştır. Karşılaştırmalar neticesinde, bu çalışma ile önerilen HGSO ayarlı PID kontrolörün uçak eğim açısı kontrol sistemi için diğer güncel ve etkili olan sezgisel-üstü algoritmalar ile ayarlanmış PID kontrolörlerine göre daha etkili olduğu ve iyi bir performansa sahip olduğu görülmüştür.

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Henry gas solubility optimization: A novel physics-based algorithm

Cited by 726 publications

References 74 publications

COVID-19 image classification using deep features and fractional-order marine predators algorithm

COVID-19 image classification using deep features and fractional-order marine predators algorithm

A Novel Metaheuristic Approach to Optimization of Neuro-Fuzzy System for Students' Performance Prediction

Henry Gaz Çözünürlük Optimizasyonu ile Uçak Eğim Kontrol Sistemi için Etkin Kontrolör Tasarımı

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