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Stefan cel Mare
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Print ISSN: 1582-7445
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doi: 10.4316/AECE


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  4/2018 - 6

Continuous Time Chaotic Systems for Whale Optimization Algorithm

TANYILDIZI, E. See more information about TANYILDIZI, E. on SCOPUS See more information about TANYILDIZI, E. on IEEExplore See more information about TANYILDIZI, E. on Web of Science, CIGAL, T. See more information about CIGAL, T. on SCOPUS See more information about CIGAL, T. on SCOPUS See more information about CIGAL, T. on Web of Science
 
Click to see author's profile in 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 (1,307 KB) | Citation | Downloads: 235 | Views: 447

Author keywords
artificial intelligence, chaos, computational intelligence, continuous time systems, whale optimization algorithm

References keywords
optimization(23), algorithm(12), chaos(11), chaotic(9), algorithms(9), systems(6), evolutionary(6), swarm(5), computation(5), applied(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-11-30
Volume 18, Issue 4, Year 2018, On page(s): 49 - 56
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.04006
Web of Science Accession Number: 000451843400006
SCOPUS ID: 85058817066

Abstract
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Discrete time chaotic systems are often used instead of random number arrays in order to improve the convergence properties of optimization algorithms and prevent them to get stuck on local solutions. In this study, discrete-time and continuous-time chaotic systems are employed to improve the performance of Whale Optimization Algorithm (WOA), for the first time. It is suggested to use continuous-time chaotic systems instead of discrete-time systems in some cases. Using 23 benchmark functions and two engineering problems, one-dimensional chaotic maps and continuous time chaotic systems were analyzed on WOA. The results show that especially in multidimensional problems the use of the continuous time chaotic system can improve the performance of the algorithm and provide faster convergence.


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References Weight

Web of Science® Citations for all references: 9,057 TCR
SCOPUS® Citations for all references: 13,327 TCR

Web of Science® Average Citations per reference: 252 ACR
SCOPUS® Average Citations per reference: 370 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 2019-10-13 15:52 in 226 seconds.




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