Metaheuristics and Swarm Methods: A Discussion on Their Performance and Applications

Cuevas, Erik; Fausto, Fernando; González, Adrián

doi:10.1007/978-3-030-16339-6_2

Cited by 6 publications

(5 citation statements)

References 112 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…\overrightarrow {D}, \overrightarrow {D}=|\overrightarrow {C}.\overrightarrow {G}^{*}(t)-{\overrightarrow {G}(t)}|\tag{1}\end{equation*} \end{document} where vector \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\overrightarrow {G}(t)$ \end{document} represents a solution at iteration \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$t$ \end{document} and vector \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\overrightarrow {G}^{*}(t)$ \end{document} represents the position of the prey. the “.” indicates pairwise multiplication and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\overrightarrow {G}{(t+1)}$ \end{document} represent the updated position for the solution [54] , [55] . The two vectors of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\overrightarrow {A}$ \end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\overrightarrow {C}$ \end{document} are updated in each iteration by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\overrightarrow {A}=2\overrightarrow {a}.\overrightarrow {r_{1}}-\overrightarrow {a}$ \end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\overrightarrow {C}=2.\overrightarrow {r_{2}}$ \end{document} for vector \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\overrightarrow {a}$ \end{document} changes from 2 to 0 linearly and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }...…”

Section: Methodsmentioning

confidence: 99%

“…the "." indicates pairwise multiplication and − → G (t + 1) represent the updated position for the solution [54], [55] The second mechanism includes a shrinking encircling, which decreases the values of − → a and − → A vectors, and a spiral process for updating the positions as follows Algorithm 1 : Original WOA Pseudo-code 1:…”

Section: Whale Optimization Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images

et al. 2020

View full text Add to dashboard Cite

Diagnosis is a critical preventive step in Coronavirus research which has similar manifestations with other types of pneumonia. CT scans and X-rays play an important role in that direction. However, processing chest CT images and using them to accurately diagnose COVID-19 is a computationally expensive task. Machine Learning techniques have the potential to overcome this challenge. This paper proposes two optimization algorithms for feature selection and classification of COVID-19. The proposed framework has three cascaded phases. Firstly, the features are extracted from the CT scans using a Convolutional Neural Network (CNN) named AlexNet. Secondly, a proposed features selection algorithm, Guided Whale Optimization Algorithm (Guided WOA) based on Stochastic Fractal Search (SFS), is then applied followed by balancing the selected features. Finally, a proposed voting classifier, Guided WOA based on Particle Swarm Optimization (PSO), aggregates different classifiers' predictions to choose the most voted class. This increases the chance that individual classifiers, e.g. Support Vector Machine (SVM), Neural Networks (NN), k-Nearest Neighbor (KNN), and Decision Trees (DT), to show significant discrepancies. Two datasets are used to test the proposed model: CT images containing clinical findings of positive COVID-19 and CT images negative COVID-19. The proposed feature selection algorithm (SFS-Guided WOA) is compared with other optimization algorithms widely used in recent literature to validate its efficiency. The proposed voting classifier (PSO-Guided-WOA) achieved AUC (area under the curve) of 0.995 that is superior to other voting classifiers in terms of performance metrics. Wilcoxon rank-sum, ANOVA, and T-test statistical tests are applied to statistically assess the quality of the proposed algorithms as well.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Whale Optimization Algorithmmentioning

confidence: 99%

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images

et al. 2020

View full text Add to dashboard Cite

show abstract

“…A large number of search algorithms for solving optimisation problem has been developed (Cuevas, et al, 2020;Whitley, 2019). In most cases, the performance of these algorithms can be further improved by introducing small modifications through adjusting their control (hyper) parameters (Vermetten, et al, 2020;El-Mihoub, et al, 2014).…”

Section: A Simple Algorithm Selector For Continuous Optimisation Prob...mentioning

confidence: 99%

Digital Twins for Lighting Analysis: Literature Review, Challenges, and Research Opportunities

Hassan¹,

Angelaki²,

Alfaro³

et al. 2022

ECMS 2022 Proceedings Edited by Ibrahim A. Hameed, Agus Hasan, Saleh Abdel-Afou Alaliyat

View full text Add to dashboard Cite

Light modelling, simulation, and photometric calculations are by now common tasks in the lighting design process. These practices contribute to the definition and comparison of suitable layout arrangements and help predict the impact of lighting devices. Those tasks demand the use of tools to support the simulation of different scenarios, the analyses of their pros and cons according to different criteria (e.g., health and safety, perception, aesthetics, energy consumption, and costs), and decision-making. Digital twins have emerged as relevant technologies to simulate and visualize different ``what-if'' scenarios associated with physical entities and processes. In this paper, we investigate the state-of-the-art research concerning the use of digital twins for supporting lighting analysis in the urban/outdoor context. We also present and discuss challenges and research opportunities related to the design, implementation, and validation of digital twins in this domain.

show abstract

Section: A Simple Algorithm Selector For Continuous Optimisation Prob...mentioning

confidence: 99%

ECMS 2022 Proceedings edited by Ibrahim A. Hameed, Agus Hasan, Saleh Abdel-Afou Alaliyat

2022

View full text Add to dashboard Cite

NTNU is a university with an international focus, with headquarters in Trondheim and campuses in Ålesund and Gjøvik. NTNU has a main profile in science and technology, a variety of programmes of professional study, and great academic breadth that also includes the humanities, social sciences, economics, medicine, health sciences, educational science, architecture, entrepreneurship, art disciplines and artistic activities. NTNU has eight faculties, 55 departments, and NTNU University Museum with a total budget of NOK 10 billion, of which NOK 2,6 billion from external sources (2021). NTNU offers 356 study programs, as well as further education to more than 42000 from Norway and abroad enrolled in various bachelor, master, and PhD programs.NTNU in its current form was established by the King-in-Council in 1996 by the merger of the former University of Trondheim and other university-level institutions, with roots dating back to 1760, and has later also incorporated some former university colleges.NTNU is consistently ranked in the top one percentage among the world's universities, We are honored to offer, besides the scientific program of the conference, consisting in more than 45 high quality accepted papers in 8 scientific tracks, a rich and various series of social events, as usual for the spirit of this conference.

show abstract

Metaheuristics and Swarm Methods: A Discussion on Their Performance and Applications

Cited by 6 publications

References 112 publications

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images

Digital Twins for Lighting Analysis: Literature Review, Challenges, and Research Opportunities

ECMS 2022 Proceedings edited by Ibrahim A. Hameed, Agus Hasan, Saleh Abdel-Afou Alaliyat

Contact Info

Product

Resources

About