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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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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.

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  3/2017 - 11

Repeating Successful Movement Strategy for ABC Algorithm

KOCER, B. See more information about KOCER, B. on SCOPUS See more information about KOCER, B. on IEEExplore See more information about KOCER, B. on Web of Science
 
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Download PDF pdficon (1,377 KB) | Citation | Downloads: 162 | Views: 350

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
Web of Science Accession Number: 000410369500011
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: 8,050 TCR
SCOPUS® Citations for all references: 13,505 TCR

Web of Science® Average Citations per reference: 268 ACR
SCOPUS® Average Citations per reference: 450 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 2018-06-15 04:32 in 158 seconds.




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Stefan cel Mare University of Suceava, Romania


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