A Novel Chaos-Enhanced Hybrid Harris Hawks Optimizer for Climate-Adaptive Solar Water Pumping: Multi-Objective Design of Cost, Reliability and Investment

# Supplementary code N. Ouazaa, S. Djeffal, "Cost, reliability and investment trade-offs of agricultural photovoltaic waterpumping in three Algerian climates: an improved multi-objective Harris hawks optimiser". MATLAB/ hourly PV pumping model, MOPSO, NSGA-II, MOABC, MOHHO, MO-IHHO, metrics, the 10 figures run main_PVpump_MOO.m (QUICK = false for the paper protocol: 10 runs x 12 000 evaluations) Python/ equivalent NumPy implementation used to produce the results and figures of the paper (fullrun.py -> analysis.py -> figs.py, tables.py; see Python/README.md) PROBLEM_FORMULATION.md decision variables, objectives, model and algorithm summary Requirements: MATLAB R2019b or newer (no toolbox); Python 3 with numpy, scipy, matplotlib. | File (MATLAB) | Role ||---|---|| pv_config.m | physical, economic and CO2 data; bounds; choice of 3rd objective (CAPEX or CO2) || pv_site.m | 864-h climate and drip-demand series of Mitidja, Constantine, Biskra || pv_simulate.m | hourly PV, hydraulics (dynamic head), tank, battery and diesel dispatch || pv_economics.m | 20-year LCC, LCOW, CAPEX, life-cycle CO2 || pv_objectives.m | objective vector and LPSP constraint || mo_pso.m, mo_nsga2.m, mo_abc.m, mo_hho.m | reference optimisers || mo_ihho.m | proposed MO-IHHO || moo_utils.m, algo_init.m | non-dominated sorting, crowding, archive, operators || moo_metrics.m, pp_metrics.m, print_tables.m | HV, IGD, IGD+, spacing, Wilcoxon, coverage, TOPSIS, tables || make_figures.m, zoom_inset.m | the 10 figures of the paper |