Data for: In Search of Excellence: SHOA as a Competitive Shrike Optimization Algorithm for Multimodal Problems

This information was collected from the Shrike Optimization Algorithm (SHOA) simulation results and compared fairly with the Ant Nesting Algorithm (ANA), Moth-Flame Optimization (MFO), Fitness Dependent Optimizer (FDO), Fox Optimization (Fox), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Black Winged Kite Algorithm (BKA), and One-to-One-Based Optimizer (OOBO). The goal is to find the best solutions to the 41 benchmark problems. The algorithm parameters were initialized, including the number of iterations required to minimize a problem, search space, problem dimensions, and control parameters. This maintains a balance between the implementation of various hardness levels of optimization problems and the impact of randomization. The outcomes of the examination were the 

Wilcoxon rank-sum test. 
Friedman means rank. 

Demonstrate that SHOA is statistically superior to the other algorithms in dealing with a variety of numerical optimization problems, including those with varying levels of hardness, problem sizes, and search spaces.