Prasatul Matrix: A Direct Comparison Approach for Analyzing Evolutionary Optimization Algorithms

TL;DR

This paper introduces the Prasatul Matrix, a novel direct comparison method for evaluating and ranking evolutionary algorithms based on their best solutions, considering both solution quality and convergence, validated through benchmark tests and statistical analysis.

Contribution

The paper proposes the Prasatul Matrix for direct comparison of solutions from evolutionary algorithms, addressing limitations of traditional statistical performance measures.

Findings

Prasatul Matrix effectively compares algorithms based on solution quality and convergence.

The approach provides a comprehensive ranking system for multiple algorithms.

Statistical tests confirm the reliability of the proposed comparison method.

Abstract

The performance of individual evolutionary optimization algorithms is mostly measured in terms of statistics such as mean, median and standard deviation etc., computed over the best solutions obtained with few trails of the algorithm. To compare the performance of two algorithms, the values of these statistics are compared instead of comparing the solutions directly. This kind of comparison lacks direct comparison of solutions obtained with different algorithms. For instance, the comparison of best solutions (or worst solution) of two algorithms simply not possible. Moreover, ranking of algorithms is mostly done in terms of solution quality only, despite the fact that the convergence of algorithm is also an important factor. In this paper, a direct comparison approach is proposed to analyze the performance of evolutionary optimization algorithms. A direct comparison matrix called \emph{Prasatul Matrix} is prepared, which accounts direct comparison outcome of best solutions obtained with two algorithms for a specific number of trials. Five different performance measures are designed based on the prasatul matrix to evaluate the performance of algorithms in terms of Optimality and Comparability of solutions. These scores are utilized to develop a score-driven approach for comparing performance of multiple algorithms as well as for ranking both in the grounds of solution quality and convergence analysis. Proposed approach is analyzed with six evolutionary optimization algorithms on 25 benchmark functions. A non-parametric statistical analysis, namely Wilcoxon paired sum-rank test is also performed to verify the outcomes of proposed direct comparison approach.