Abstract This study presents a Chaos-driven Optimization (CDO) algorithm, a novel metaheuristic algorithm designed to enhance global search performance, convergence speed, and solution robustness. The algorithm incorporates chaos theory by initially evaluating ten distinct chaotic maps as candidate generators, from which the Logistic map is ultimately selected for the final CDO implementation based on systematic performance analysis, enabling an effective balance between exploration and exploitation while reducing premature convergence. The effectiveness of CDO is evaluated using 35 standard benchmark functions, including 23 from CEC-2005 and 12 from CEC-2022. Comparative analysis against state-of-the-art optimizers demonstrates that CDO consistently outperforms or achieves competitive results. Statistical validation using mean and standard deviation, complemented by Friedman ranking and post-hoc Nemenyi significance analysis, confirms the proposed algorithm’s statistically consistent superiority and robustness. To demonstrate practicability, CDO is applied to the optimal sizing of an off-grid Hybrid Renewable Energy Systems (HRES) for the rural community of Abrafo, Ghana, using one year of real load and meteorological data. The system integrates photovoltaic arrays, wind turbines, biomass generators, battery storage, electrolyzers, hydrogen tanks, and fuel cells. Optimization is performed under multiple reliability levels defined by the Loss of Power Supply Probability (LPSP). Results show that CDO produces economically viable, technically feasible, and reliable system configurations, highlighting its strong potential for sustainable off-grid planning.
Blankson et al. (Sun,) studied this question.