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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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2019-Jun-20
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  4/2017 - 8

Particle Swarm Optimization with Power-Law Parameter Based on the Cross-Border Reset Mechanism

WANG, H. See more information about WANG, H. on SCOPUS See more information about WANG, H. on IEEExplore See more information about WANG, H. on Web of Science, FEI, Y. See more information about  FEI, Y. on SCOPUS See more information about  FEI, Y. on SCOPUS See more information about FEI, Y. on Web of Science, LI, Y. See more information about  LI, Y. on SCOPUS See more information about  LI, Y. on SCOPUS See more information about LI, Y. on Web of Science, REN, S. See more information about  REN, S. on SCOPUS See more information about  REN, S. on SCOPUS See more information about REN, S. on Web of Science, CHE, J. See more information about  CHE, J. on SCOPUS See more information about  CHE, J. on SCOPUS See more information about CHE, J. on Web of Science, XU, H. See more information about XU, H. on SCOPUS See more information about XU, H. on SCOPUS See more information about XU, H. on Web of Science
 
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Download PDF pdficon (1,700 KB) | Citation | Downloads: 316 | Views: 833

Author keywords
evolutionary computation, optimization, particle swarm optimization, performance evaluation, benchmark testing

References keywords
swarm(23), optimization(22), algorithm(10), levy(9), evolutionary(9), computation(8), intelligence(6), flight(6), computing(6), applied(6)
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): 59 - 68
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.04008
Web of Science Accession Number: 000417674300008
SCOPUS ID: 85035775186

Abstract
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In order to improve the performance of traditional particle swarm optimization, this paper introduces the principle of Levy flight and cross-border reset mechanism. In the proposed particle swarm optimization, the dynamic variation of parameters meets the power-law distribution and the pattern of particles transition conforms to the Levy flight in the process of algorithm optimization. It means the particles make long distance movements in the search space with a small probability and make short distance movements with a large probability. Therefore, the particles can jump out of local optimum more easily and coordinate the global search and local search of particle swarm optimization. This paper also designs the cross-border reset mechanism to make particles regain optimization ability when stranding on the border of search space after a long distance movement. The simulation results demonstrate the proposed algorithms are easier to jump out of local optimum and have higher accuracy when compared with the existing similar algorithms based on benchmark test functions and handwriting character recognition system.


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References Weight

Web of Science® Citations for all references: 5,719 TCR
SCOPUS® Citations for all references: 6,019 TCR

Web of Science® Average Citations per reference: 151 ACR
SCOPUS® Average Citations per reference: 158 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 2019-08-21 00:19 in 245 seconds.




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