A novel improved antlion optimizer algorithm and its comparative performance

Kilic, Haydar; Yüzgeç, Uğur; Karakuzu, Cihan

doi:10.1007/s00521-018-3871-9

Cited by 29 publications

(13 citation statements)

References 37 publications

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“…Therefore, metaheuristic algorithms frequently employ randomization techniques, and their performance depends on the appropriate use of such randomization (Yang 2014). ALO algorithm consumed very long time which was a nature result of the random ant walking it performs, (Kiliç et al 2018). Figure 4 shows the dramatic difference in time when using the ant lion algorithm, generating 951 training rules, while the GNRCS still showed its superiority in generating the least number of rules, 707 rules, performed in 1140 sec compared to the ALONRCS generating 951 rules in 24480 sec.…”

Section: Nrcs Vs Gnrcsmentioning

confidence: 99%

RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

et al. 2021

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The highly spreading virus, COVID-19, created a huge need for an accurate and speedy diagnosis method. The famous RT-PCR test is costly and not available for many suspected cases. This article proposes a neurotrophic model to diagnose COVID-19 patients based on their chest X-ray images. The proposed model has five main phases. First, the speeded up robust features (SURF) method is applied to each X-ray image to extract robust invariant features. Second, three sampling algorithms are applied to treat imbalanced dataset. Third, the neutrosophic rule-based classification system is proposed to generate a set of rules based on the three neutrosophic values < T; I; F>, the degrees of truth, indeterminacy falsity. Fourth, a genetic algorithm is applied to select the optimal neutrosophic rules to improve the classification performance. Fifth, in this phase, the classification-based neutrosophic logic is proposed. The testing rule matrix is constructed with no class label, and the goal of this phase is to determine the class label for each testing rule using intersection percentage between testing and training rules. The proposed model is referred to as GNRCS. It is compared with six state-of-the-art classifiers such as multilayer perceptron (MLP), support vector machines (SVM), linear discriminant analysis (LDA), decision tree (DT), naive Bayes (NB), and random forest classifiers (RFC) with quality measures of accuracy, precision, sensitivity, specificity, and F1-score. The results show that the proposed model is powerful for COVID-19 recognition with high specificity and high sensitivity and less computational complexity. Therefore, the proposed GNRCS model could be used for real-time automatic early recognition of COVID-19.

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Section: Nrcs Vs Gnrcsmentioning

confidence: 99%

RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

et al. 2021

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“…In terms of discovery and exploitation, ALO gives superior results [21]. Good exploration means that the possible areas of the search space are adequately investigated and prevents the local optima from trapping the algorithm, which is ensured by 8 Gagnesh Kumar 1 * and Sunil Agrawal 2 * Correspondence: gagnesh85@gmail.com the random walks of ants near the antlion and the random selection of antlions.…”

Section: Ant Lion Optimizationmentioning

confidence: 99%

Ant Lion Optimizer for Suppression of Ambipolar Conduction in Schottky Barrier Carbon Nanotube Field Effect Transistors

Kumar

Agrawal

2021

Silicon

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A mathematical model and experimental analysis of the effect of oxide thickness on ambient conduction is provided in the Schottky Barrier Carbon Nanotubes (CNTs) Field Effect Transistor (SB-CNTFET). To develop them as the future of IC (integrated circuit) technology, the suppression of ambipolar behaviour in SB-CNTFET is imperative. The ambipolar nature of SB-CNTFET contributes to a high amount of leakage current. tox ≈ 49.91mm uses a dielectric of gate oxide with a thickness to inhibit the ambipolar behaviour. In an SB-CNTFET, the conductance is regulated by the electrical field at the source/drain contacts and the band bending length at the contacts is determined by t ox . Therefore, the prime parameter t ox that affects the Schottky barrier width and the subthreshold area. The suppression of ambipolar property is presented through experimental analysis. The SB-CNTFET is produced using high-k dielectrics such as Zirconium dioxide. This work discusses the suppression of ambipolar activity in SB-CNTFETs without reducing the I on current using an appropriate dielectric with optimum thickness.

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“…ANFIS is based on that a fuzzy system is trained by a learning algorithm derived from the neural network. These systems are capable of modeling the nonlinear relation between input and output of a system [19]. In the literature, there are different ANFIS applications in areas such as environmental engineering [37], health informatics [7], earth sciences [20], agricultural & biosystems engineering [33], synthesis of production processes [15].…”

Section: Introductionmentioning

confidence: 99%

“…Karaboga and his colleagues compared learning performance of ANFIS using ABC algorithm, GA, backpropagation (BP) and hybrid learning (HL) in 2013 [11]. Kilic and his friends proposed the improved ALO algorithm via a tournament selection method for optimizing the parameters used in ANFIS [19].…”

Section: Introductionmentioning

confidence: 99%

Improved Antlion Optimizer Algorithm and Its Performance on Neuro Fuzzy Inference System

Kilic¹,

Yüzgeç²,

Karakuzu³

2019

NNW

Self Cite

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Antlion optimizer algorithm (ALO) is inspired by hunting strategy of antlions. In this study, an improved antlion optimization algorithm is proposed for training parameters of adaptive neuro fuzzy inference system (ANFIS). In the standard ALO algorithm, the greatest deficiency is its long running time during optimization process. The random walking model of ants, the selection procedure and boundary checking mechanism have been developed to speed up standard ALO algorithm. To evaluate the performance of the improved antlion optimization algorithm (IALO), it has been tested on dynamic system modelling problems. ANFIS's parameters has been optimized by IALO algorithm to model five dynamic systems. ANFIS training procedure has been performed with 30 independent runs. Each training has been started with the random initial parameters of ANFIS and performance metrics have been obtained at the end of training. The results show that the IALO algorithm is able to provide competitive results in terms of mean, best, worst, standard deviation, training time metrics. According to the training time result, the proposed IALO algorithm has better performance than standard ALO algorithm and the average training time has been reduced to approximately 80 %.

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A novel improved antlion optimizer algorithm and its comparative performance

Cited by 29 publications

References 37 publications

RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

Ant Lion Optimizer for Suppression of Ambipolar Conduction in Schottky Barrier Carbon Nanotube Field Effect Transistors

Improved Antlion Optimizer Algorithm and Its Performance on Neuro Fuzzy Inference System

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