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

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.

本信息源自Shrike优化算法（SHOA）的仿真结果，并与蚁巢算法（ANA）、蛾火焰优化（MFO）、适应性依赖优化器（FDO）、狐狸优化（Fox）、粒子群优化（PSO）、遗传算法（GA）、黑翅风筝算法（BKA）以及一对一优化器（OOBO）进行了较为公正的比较。研究旨在寻找解决41个基准问题最佳解决方案。算法参数初始化包括用于最小化问题的迭代次数、搜索空间、问题维度和控制参数。此过程旨在平衡优化问题各种难度级别实现与随机化影响之间的均衡。检验结果包括威尔科克森符号秩检验和弗里德曼均值秩检验。验证SHOA在处理包括不同难度级别、问题规模和搜索空间的各类数值优化问题方面的统计优越性。