MATLAB Source Code for the Gadaa Optimization Algorithm (GOA): A Governance-Inspired Metaheuristic for Global Optimization

Description: This repository contains the complete MATLAB implementation of the Gadaa Optimization Algorithm (GOA), a novel population-based metaheuristic derived from the Oromo Gadaa governance system of Ethiopia, as described in the accompanying manuscript submitted to Applied Soft Computing (Elsevier). Contents: GOA_fixed.m — Main GOA implementation including all eight governance operators (Gogessa, Butta, Yuba, Gumii Gayo, Siinqee, Abbaa Dula, ally sharing, Deb feasibility ordering) CMAES.m — CMA-ES comparison algorithm implementation run_01_full_benchmark_N50.m — Classical benchmark experiments (Sphere, Rosenbrock, Rastrigin, Ackley, Griewank; D=10,30,50,100; N=50; 30 runs) run_02_scalability_D100_N50.m — Scalability analysis up to D=100 run_03_engineering_N50.m — Six constrained engineering design problems run_04_diversity_N50.m — Population diversity analysis run_05_runtime_N50.m — Wall-clock runtime comparison run_06_parameter_sensitivity.m — Parameter sensitivity analysis (K, T_B, N_Y) run_06b_sensitivity_figures.m — Sensitivity figure generation run_07b_ELD_fixed.m — Economic Load Dispatch case study (6-generator, 1263 MW) run_08_friedman_holm.m — Friedman rank test and Holm post-hoc statistical analysis run_09_all_figures.m — Main results figure generation run_10_3D_surfaces.m — 3D benchmark function surface plots run_11_mechanism_figures.m — Mechanistic analysis figures run_12_niching_full.m — Natural niching / basin coverage study run_12b_niching_DE.m — Extended niching study including DE Requirements: MATLAB R2020a or later. No additional toolboxes required. Note: Comparison algorithm implementations for PSO, GWO, WOA, and DE follow standard published formulations and are not redistributed here.