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.

本研究数据取自伯劳优化算法（Shrike Optimization Algorithm, SHOA）的仿真结果，并与蚁巢优化算法（Ant Nesting Algorithm, ANA）、飞蛾火焰优化算法（Moth-Flame Optimization, MFO）、适应度依赖优化算法（Fitness Dependent Optimizer, FDO）、狐狸优化算法（Fox Optimization）、粒子群优化算法（Particle Swarm Optimization, PSO）、遗传算法（Genetic Algorithm, GA）、黑鸢优化算法（Black Winged Kite Algorithm, BKA）以及一对一优化算法（One-to-One-Based Optimizer, OOBO）开展了公平对比。本次研究的核心目标是为41个基准测试问题寻得最优解。所有算法参数均完成初始化，包括优化问题所需迭代次数、搜索空间、问题维度以及控制参数，以此平衡不同难度层级优化问题的求解复杂度与随机化操作的影响。本次检验采用威尔科克森秩和检验（Wilcoxon rank-sum test）与弗里德曼平均秩次（Friedman means rank）作为评估指标，实验结果表明，在处理各类数值优化问题（涵盖不同难度等级、问题规模与搜索空间的优化任务）时，伯劳优化算法在统计学层面的性能优于其余对比算法。