BENCHMARK TESTING FOR WHALE OPTIMIZATION ALGORITHM AND PARTICLE SWARM OPTIMIZATION
DESIGN DETAILS
The performance of the WOA and PSO tested on benchmark test functions used in the CEC-2017. The average (Ave) and standard deviation (Std) score metrics were used as performance assessment indicators for the accuracy and stability of the algorithms. In this work, these statistical evaluation measures were computed for the algorithm in each function as the two best solutions. The mean measure was used to evaluate the accuracy of the algorithms, while the analysis of the standard deviation results aims to ensure stable performance of the algorithms during the independent runs. The best results are reinforced for all test problems throughout is to give them more prominence out of other results.
Friedman’s test applied to the mean error results of the CEC-2017 benchmark with dimensions 30 for all functions. The p-value revealed by Friedman’s test on the results of the CEC-2017 functions with a dimension of 30. This p-value indicates that there is a statistically large difference between the algorithms, which means that the null hypothesis is rejected.
Holm’s test for the CEC-2017 test functions with dimensions of 30 for each function, the hypotheses with p-value ≤ 0.0125 were rejected by the Holm’s test method.
Statistical results and optimization results of WOA and PSO over 51 independent runs on the CEC-2017 30 test functions of 10 variables with 100,000 Fes is performed.
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