Regression line shifting mechanism for analyzing evolutionary optimization algorithms
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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.
KeywordsEvolutionary optimization algorithms Linear regression Particle swarm optimization Differential evolution Visual analysis
Part of the work has been appeared at ISCBI 2014 (Biswas and Biswas 2014). Proper citations have been included for the same in the above work for the purpose transparency.
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Conflict of interest
The authors declare that they have no conflict of interest.
Also informed consent was obtained from all individual participants included in the study.
Human and animal rights
This article does not contain any studies with human participants or animals performed by any of the authors.
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