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JCR Impact Factor: 0.700
JCR 5-Year IF: 0.700
SCOPUS CiteScore: 1.8
Issues per year: 4
Current issue: Nov 2024
Next issue: Feb 2025
Avg review time: 58 days
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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  4/2017 - 11

Developing Automatic Multi-Objective Optimization Methods for Complex Actuators

CHIS, R. See more information about CHIS, R. on SCOPUS See more information about CHIS, R. on IEEExplore See more information about CHIS, R. on Web of Science, VINTAN, L. See more information about VINTAN, L. on SCOPUS See more information about VINTAN, L. on SCOPUS See more information about VINTAN, L. on Web of Science
 
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Download PDF pdficon (1,410 KB) | Citation | Downloads: 864 | Views: 2,635

Author keywords
actuators, computer aided engineering, machine learning, pareto optimization, response surface methodology

References keywords
optimization(11), design(9), systems(7), multi(7), vintan(5), computing(5), objective(4), multiobjective(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-11-30
Volume 17, Issue 4, Year 2017, On page(s): 89 - 98
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.04011
Web of Science Accession Number: 000417674300011
SCOPUS ID: 85035746256

Abstract
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This paper presents the analysis and multiobjective optimization of a magnetic actuator. By varying just 8 parameters of the magnetic actuators model the design space grows to more than 6 million configurations. Much more, the 8 objectives that must be optimized are conflicting and generate a huge objectives space, too. To cope with this complexity, we use advanced heuristic methods for Automatic Design Space Exploration. FADSE tool is one Automatic Design Space Exploration framework including different state of the art multi-objective meta-heuristics for solving NP-hard problems, which we used for the analysis and optimization of the COMSOL and MATLAB model of the magnetic actuator. We show that using a state of the art genetic multi-objective algorithm, response surface modelling methods and some machine learning techniques, the timing complexity of the design space exploration can be reduced, while still taking into consideration objective constraints so that various Pareto optimal configurations can be found. Using our developed approach, we were able to decrease the simulation time by at least a factor of 10, compared to a run that does all the simulations, while keeping prediction errors to around 1%.


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Cited-By CrossRef

[1] A competitive new multi-objective optimization genetic algorithm based on apparent front ranking, Neghină, Mihai, Dicoiu, Alina-Ioana, Chiş, Radu, Florea, Adrian, Engineering Applications of Artificial Intelligence, ISSN 0952-1976, Issue , 2024.
Digital Object Identifier: 10.1016/j.engappai.2024.107870
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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