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.

该论文提出一种直接比较演化优化算法性能的方法，通过构建名为 Prasatul Matrix 的直接比较矩阵和五种性能评估指标，既可以比较多个算法的性能，也能评估每个算法的优化和可比性解决方案，同时使用 Wilcoxon 配对秩和检验来验证该方法的结果。