Moth–flame optimization algorithm: variants and applications

Shehab, Mohammad; Abualigah, Laith; Hamad, Husam Al; Alabool, Hamzeh; Khasawneh, Ahmad M.

doi:10.1007/s00521-019-04570-6

Cited by 220 publications

(87 citation statements)

References 108 publications

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“…give more attention to hybrid AI systems that combined new trends of AI techniques (e.g., metaheuristic algorithm) to bene t from the different characteristics of different algorithms. For example, but not limited to, salp swarm algorithm (Abualigah et al, 2019), moth-ame optimization (Shehab et al,2019), and cuckoo search algorithm (Shehab et al,2017) are a very promising and interesting algorithm that has already been successfully applied to several problems in medical research area due to their contain simplicity, speed in searching, and simple hybridization with other algorithms.…”

Section: Should Develop Hybrid Ai Systemsmentioning

confidence: 99%

Artificial intelligence techniques for Containment COVID-19 Pandemic: A Systematic Review

Alabool

Alarabiat

Habib

et al. 2020

Preprint

Self Cite

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Due to the advantages offered by AI in containment the COVID-19 pandemic, the number of AI techniques has increased greatly. Although these techniques provide an acceptable start to COVID-19 pandemic control, they differ in terms of purpose, AI synthesis methods, datasets, validation approach. This increase and diversity in the numbers of proposed AI techniques can confuse decision makers and lead them to the dilemma of what is the appropriate technique under the specific conditions. Yet, studies that assess, analyze, and summarize the unresolved problems and shortcomings of current AI techniques for COVID-19 are limited. In the existing review studies, only individual parts of AI techniques, rarely the full solution, are reviewed and examined. Thus, this study aims to present a comprehensive systematic review on the application of AI techniques in containment the COVID-19 pandemic. The applied search strategy led to include 73 papers related to the Application of AI techniques for COVID-19 published from December 2019 to May 2020. Ten applications of AI for containment COVID-19 were identified. In addition, the analysis results of the systematic review revealed five deficiencies so that future research should take them into consideration.

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Section: Should Develop Hybrid Ai Systemsmentioning

confidence: 99%

Artificial intelligence techniques for Containment COVID-19 Pandemic: A Systematic Review

Alabool

Alarabiat

Habib

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…For years the expert designed features played a predominating role, giving the field in recent years to the learned features by the deep neural architectures. The subject is very broad and here we only scratch the surface by providing some examples [1,2,21,22,25,34,35,42].…”

Section: Features Acquisition and Object Detectionmentioning

confidence: 99%

Fast eyes detection in thermal images

Knapik

Cyganek

2020

Multimed Tools Appl

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In recent years many methods have been proposed for eye detection. In some cases however, such as driver drowsiness detection, lighting conditions are so challenging that only the thermal imaging is a robust alternative to the visible light sensors. However, thermal images suffer from poor contrast and high noise, which arise due to the physical properties of the long waves processing. In this paper we propose an efficient method for eyes detection based on thermal image processing which can be successfully used in challenging environments. Image pre-processing with novel virtual high dynamic range procedure is proposed, which greatly enhances thermal image contrast and allows for more reliable computation of sparse image descriptors. The bag-of-visual-words approach with clustering was selected for final detections. We compare our method with the YOLOv3 deep learning model. Our method attains high accuracy and fast response in real conditions without computational complexity and requirement of a big dataset associated with the deep neural networks. For quantitative analysis a series of thermal video sequences were recorded in which eye locations were manually annotated. Created dataset was made publicly available on our website.

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“…Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2020.3019445, IEEE Access = sum of results, ( 1,2,3) i ki = = means of every level. max = the results of maximal path length and min = the results of minimal path length.…”

Section: A Parameter Setting Of the Algorithmmentioning

confidence: 99%

“…They usually do not rely on the specific conditions of some problems, so they can be applied to a broader area. Today, the Meta-heuristic algorithm has been successfully applied in engineering, computer network, biological system modeling, forecasting, pattern recognition, data clustering, feature selection, and other fields [1][2][3][4]. Meta-heuristic algorithms are classified into local search-based algorithms and population-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

High-Frequency Path Mining-Based Reward and Punishment Mechanism for Multi-Colony Ant Colony Optimization

2020

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To solve the problem of falling into local optimum and poor convergence speed of traditional ant colony algorithm, this paper proposes a High-frequency path mining-based Reward and Punishment mechanism for multi-colony Ant Colony Optimization (HRPACO). Firstly, the pheromone concentration on the path of effective strong association is rewarded adaptively according to the lift of association rules to accelerate the convergence speed. Secondly, the pheromone concentration on the path of minimum spanning tree is punished adaptively according to the support of association rules to improve the diversity of the colony. The interaction of reward and punishment mechanism can effectively balance the diversity and convergence. Finally, a self-evolutionary mechanism based on Gaussian filter is proposed to adaptively adjust the pheromone concentration by dynamic smoothing of the pheromone matrix, so as to help the colony jump out of the local optimum. The TSP is used to verify the performance of the algorithm. The simulation results show that the proposed algorithm can effectively accelerate the convergence speed and improve the accuracy of solution, especially for large-scale problems. Meanwhile, path planning is used to verify the feasibility of the proposed algorithm. The simulation results show that the algorithm can find an effective and better path even in the environment of complex obstacles.

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Moth–flame optimization algorithm: variants and applications

Cited by 220 publications

References 108 publications

Artificial intelligence techniques for Containment COVID-19 Pandemic: A Systematic Review

Artificial intelligence techniques for Containment COVID-19 Pandemic: A Systematic Review

Fast eyes detection in thermal images

High-Frequency Path Mining-Based Reward and Punishment Mechanism for Multi-Colony Ant Colony Optimization

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