Soft Computing

, Volume 21, Issue 21, pp 6237–6252 | Cite as

Regression line shifting mechanism for analyzing evolutionary optimization algorithms

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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.

Keywords

Evolutionary optimization algorithms Linear regression Particle swarm optimization Differential evolution Visual analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology (BHU)VaranasiIndia

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