This work introduces a novel methodology to perform the comparative analysis of evolutionary optimiza-tion 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 qual-ity 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 algo-rithms in terms of both solution quality and convergence rate. Method is analyzed with well-established algorithms
and real data obtained from 25 benchmark functions.

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