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

Published: 18 November 2024| Version 1 | DOI: 10.17632/dk2djtfcdg.1
Contributors:
Hanan Abdulkarim,

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.

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Categories

Algorithms, Theoretical Computer Science, Analysis of Algorithm, Evolutionary Computation, Constrained Optimization

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