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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: Aug 2017
Next issue: Nov 2017
Avg review time: 76 days


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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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FEATURED ARTICLE

ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
Issue 3/2016

AbstractPlus






LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

2017-Jan-30
We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

Read More »


    
 

  3/2017 - 11

Repeating Successful Movement Strategy for ABC Algorithm

KOCER, B.
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,377 KB) | Citation | Downloads: 38 | Views: 53

Author keywords
artificial intelligence, machine learning, evolutionary computation, particle swarm optimization, machine intelligence

References keywords
optimization(18), algorithm(18), artificial(13), colony(12), comput(8), jasoc(6), swarm(5), soft(5), search(5), jins(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-08-31
Volume 17, Issue 3, Year 2017, On page(s): 85 - 94
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.03011
SCOPUS ID: 85028548845

Abstract
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ABC is a well-known nature inspired algorithm. In short ABC algorithm mimics the foraging behavior of the bee colonies. ABC is very intensively worked algorithm. It has many variants. The base algorithm and most of the variants uses an update equation to improve the solutions. The update equation finds a feasible movement based on neighbor solutions and adds that movement to current to create a mutant solution. If the mutant solution is better than the original one then original solution is updated. None of the ABC variant use a successful movement again. In this work when a successful move found then it is used again. Proposed approach is applied to ABCVSS algorithm which is a recently proposed ABC variant and that modified ABCVSS algorithm (ABCVSSRSM) is tested on numerical benchmark functions and results compared the well-known ABC variants. Results show that proposed method is superior under multiple criteria.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 6,572 TCR
SCOPUS® Citations for all references: 12,187 TCR

Web of Science® Average Citations per reference: 219 ACR
SCOPUS® Average Citations per reference: 406 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-09-26 01:20 in 156 seconds.




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