An innovative metaheuristic algorithm for photovoltaic tilt angle optimization

Optimizing photovoltaic tilt angles to maximize solar radiation capture remains a critical and challenging task. This paper proposes HMWOAIGWO, a novel hybrid metaheuristic algorithm that integrates the improved grey wolf optimizer (IGWO) with the whale optimization algorithm (WOA). The proposed algorithm aims to optimize tilt angles on daily, monthly, and annual scales while addressing the limitations of individual methods, including limited population diversity, susceptibility to local optima, and slow convergence rates. The performance of HMWOAIGWO was rigorously evaluated against ten state-of-the-art algorithms using 23 benchmark suites and the CEC 2019 test functions. Results indicate that HMWOAIGWO achieved the highest accuracy on 19 out of 33 functions and ranked within the top two for convergence speed in 78% of the test functions (18/23). In addition, across five real-world optimization problems, the algorithm attained the lowest standard deviation in all cases(100%) and outperformed competitors in mean performance on 60% of the problems. Statistical validation via Wilcoxon and Friedman tests confirm that the statistical results significantly improve the optimality of the solutions obtained by HMWOAIGWO. Applied to photovoltaic systems, it yielded improvements in solar radiation capture of 4%, 1.76%, and 0.96% for daily, monthly, and annual tilt optimizations, respectively. These findings demonstrate the algorithm's capability to effectively balance exploration and exploitation, making it a robust tool for complex, real-world photovoltaic optimization challenges.