ADR-SSA: An Enhanced Sparrow Search Algorithm for Complex Optimization and Data-Driven Forecasting (Execution Code)
MATLAB execution code accompanying the manuscript "ADR-SSA: An Enhanced Sparrow Search Algorithm for Complex Optimization and Data-Driven Forecasting" . ADR-SSA enhances the Sparrow Search Algorithm with two coordinated mechanisms: dynamic reverse learning, which repositions the worst-performing individuals relative to the evolving search region, and adaptive t-distribution mutation, which perturbs elite individuals with a scale that adapts during iteration. Contents: the ADR-SSA implementation ( code/ADR_SSA.m ); CEC2022 comparison and component-ablation scripts ( Ablation.m , CEC2022_DRL_ATM_ExcelOnly.m ); constrained engineering design exp
Resource information
- Format
- zip
- Original repository
- 23262819
- DOI
- 10.5281/zenodo.23262819
- Credit basis
- Creators in Zenodo record metadata
- Access
- Open files available from the original Zenodo deposit
- Last checked
- Nov 5, 2026
- License
- cc-by-4.0
- Last record update
- Nov 5, 2026
About this resource
MATLAB execution code accompanying the manuscript "ADR-SSA: An Enhanced Sparrow Search Algorithm for Complex Optimization and Data-Driven Forecasting" . ADR-SSA enhances the Sparrow Search Algorithm with two coordinated mechanisms: dynamic reverse learning, which repositions the worst-performing individuals relative to the evolving search region, and adaptive t-distribution mutation, which perturbs elite individuals with a scale that adapts during iteration. Contents: the ADR-SSA implementation ( code/ADR_SSA.m ); CEC2022 comparison and component-ablation scripts ( Ablation.m , CEC2022_DRL_ATM_ExcelOnly.m ); constrained engineering design experiments on the welded beam and pressure vessel problems with seven optimizers ( engineering.m , plot_engineering.m ); convergence-curve plotting for the forecasting experiments ( machine.m ); population diversity / exploration-exploitation analysis ( Dive_Explor_Exploit.m ); the compiled CEC2022 benchmark suite for Windows x64 ( cec22_func.mexw64 ); and utility functions for initialization and Levy flights. Requirements: MATLAB (R2020a or later), Statistics and Machine Learning Toolbox, and Microsoft Windows x64 for the bundled benchmark MEX file. Comparison optimizers and the CEC2022 toolbox sources are third-party and are not redistributed; see the repository README for the list of files to add before running the experiments.