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Soft Computing
Erschienen in: Soft Computing 21/2017

21.09.2016 | Foundations

Regression line shifting mechanism for analyzing evolutionary optimization algorithms

verfasst von: Anupam Biswas, Bhaskar Biswas

Erschienen in: Soft Computing | Ausgabe 21/2017

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Abstract

This work introduces a novel methodology to perform the comparative analysis of evolutionary optimization algorithms. The methodology relies simply on linear regression and quantile–quantile plots. The methodology is extrapolated as the one-to-one comparison, one-to-many comparison and many-to-many comparison of solution quality and convergence rate. Most of the existing approaches utilize both solution quality and convergence rate to perform comparative analysis. However, many-to-many comparison, i.e., ranking of algorithms is done only in terms of solution quality. The proposed method is capable of ranking algorithms in terms of both solution quality and convergence rate. Method is analyzed with well-established algorithms and real data obtained from 25 benchmark functions.
Vorheriger Artikel An ant-based QoS-aware routing protocol for heterogeneous wireless sensor networks
Nächster Artikel A hybrid model of single valued neutrosophic sets and rough sets: single valued neutrosophic rough set model

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Literatur
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Metadaten
Titel
Regression line shifting mechanism for analyzing evolutionary optimization algorithms
verfasst von
Anupam Biswas
Bhaskar Biswas
Publikationsdatum
21.09.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 21/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-016-2355-z

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