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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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  2/2013 - 10

Comparing the Robustness of Evolutionary Algorithms on the Basis of Benchmark Functions

DENIZ ULKER, E. See more information about DENIZ ULKER, E. on SCOPUS See more information about DENIZ ULKER, E. on IEEExplore See more information about DENIZ ULKER, E. on Web of Science, HAYDAR, A. See more information about HAYDAR, A. on SCOPUS See more information about HAYDAR, A. on SCOPUS See more information about HAYDAR, A. on Web of Science
 
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Download PDF pdficon (702 KB) | Citation | Downloads: 370 | Views: 1,868

Author keywords
computational intelligence, evolutionary computation, heuristic algorithms

References keywords
optimization(14), evolutionary(11), computation(9), algorithm(9), search(7), algorithms(7), harmony(6), applied(6), swarm(5), geem(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2013-05-31
Volume 13, Issue 2, Year 2013, On page(s): 59 - 64
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.02010
Web of Science Accession Number: 000322179400010
SCOPUS ID: 84878946831

Abstract
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In real-world optimization problems, even though the solution quality is of great importance, the robustness of the solution is also an important aspect. This paper investigates how the optimization algorithms are sensitive to the variations of control parameters and to the random initialization of the solution set for fixed control parameters. The comparison is performed of three well-known evolutionary algorithms which are Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm and the Harmony Search (HS) algorithm. Various benchmark functions with different characteristics are used for the evaluation of these algorithms. The experimental results show that the solution quality of the algorithms is not directly related to their robustness. In particular, the algorithm that is highly robust can have a low solution quality, or the algorithm that has a high quality of solution can be quite sensitive to the parameter variations.


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

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[CrossRef] [SCOPUS Times Cited 2054]


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[CrossRef] [Full Text] [Web of Science Times Cited 20] [SCOPUS Times Cited 25]


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[CrossRef] [Web of Science Times Cited 840] [SCOPUS Times Cited 1230]


[9] Y. Shi, R. Eberhart, "Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization", Proceedings of the Congress on Evolutionary Computation, 2000, pp. 84-88.
[CrossRef]


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[CrossRef] [SCOPUS Times Cited 5]


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[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]


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[CrossRef]


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[22] R. Storn, "Differential Evolution Design of an IIR-filter", Evolutionary Computation IEEE, 1996, pp. 268-273.
[CrossRef]


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[CrossRef]


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[CrossRef]




References Weight

Web of Science® Citations for all references: 25,089 TCR
SCOPUS® Citations for all references: 5,633 TCR

Web of Science® Average Citations per reference: 929 ACR
SCOPUS® Average Citations per reference: 209 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 background updated on 2017-02-24 19:09 in 165 seconds.




Note1: Web of Science® is a registered trademark of Thomson Reuters.
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Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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