A Hybrid Simulated Annealing-Particle Swarm Algorithm for MPPT in PV Systems

To address the issue of maximum power point tracking (MPPT) in photovoltaic (PV) systems prone to local optima under complex lighting conditions, this paper proposes a temperature-regulated Simulated Annealing-Particle Swarm Hybrid Algorithm (SA-PSO). The algorithm integrates the global search capability of simulated annealing with the efficient parallel characteristics of particle swarm optimization (PSO). By implementing a temperature-driven adaptive adjustment of inertia weight and an intelligent restart mechanism, it balances global exploration and local exploitation, enhancing tracking robustness in dynamic environments. Results from test functions and MATLAB simulations demonstrate that SA-PSO significantly outperforms conventional algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), and incremental conductance method (INC) in convergence speed, accuracy, and the ability to avoid local optima, providing an effective solution to improve the energy conversion efficiency of PV systems.