Data for: Statistical Analysis and Performance Evaluation of Shrike Optimization Algorithm (SHOA)
Description
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.