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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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  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
 
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Download PDF pdficon (1,307 KB) | Citation | Downloads: 652 | Views: 1,421

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.


References | Cited By

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

SCOPUS® Times Cited: 2
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Cited-By CrossRef

[1] An urban short-term traffic flow prediction model based on wavelet neural network with improved whale optimization algorithm, Du, Wangdi, Zhang, Qingyong, Chen, Yuepeng, Ye, Ziliu, Sustainable Cities and Society, ISSN 2210-6707, Issue , 2021.
Digital Object Identifier: 10.1016/j.scs.2021.102858
[CrossRef]

[2] Embedded chaotic whale survival algorithm for filter–wrapper feature selection, Guha, Ritam, Ghosh, Manosij, Mutsuddi, Shyok, Sarkar, Ram, Mirjalili, Seyedali, Soft Computing, ISSN 1432-7643, Issue 17, Volume 24, 2020.
Digital Object Identifier: 10.1007/s00500-020-05183-1
[CrossRef]

Updated 3 days, 15 hours ago

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