A novel star-nosed mole optimization algorithm applied for MPPT of PV systems

Star-nosed mole optimization (SNMO) algorithm is a novel metaheuristic algorithm inspired by the unique foraging behavior of the star-nosed mole. This animal's exceptional ability to rapidly sense and process information from its environment has been translated into a powerful optimization algorithm in this study. SNMO leverages the mole's parallel exploration, focused foraging, and adaptive movement strategies to efficiently search for optimal solutions. The algorithm initializes a population of candidate solutions and iteratively refines them through a process of sensory exploration, promising region prioritization, and redundancy avoidance. SNMO's key features include its ability to balance exploration and exploitation, adapt to dynamic environments, and efficiently converge to optimal solutions. This paper presents the core principles, mathematical formulation, and potential applications of the SNMO algorithm, showcasing its effectiveness in solving a wide range of optimization problems especially the maximum power point tracker (MPPT) of PV systems. The power-voltage characteristics of the PV arrays under partial shading conditions (PSCs) which have several peaks show an amazing idea to send each mole to the vicinity of each peak to separately determine the peaks around it. Once moles determine the peaks, they will be attracted to the highest one. This algorithm has been examined in grid-connected PV systems through simulation and experimental work and showed superior convergence speed and accuracy performance. Future improvements to this novel optimization algorithm can lead to further improvement to the MPPT of PV systems and other applications.