Parameter Optimization Method for the Free-Standing Magnetic Field Energy Harvester Based on an Improved Multi-Objective Particle Swarm Optimization Algorithm

The free-standing magnetic field energy harvester (FSMFEH) is an effective solution to the power supply challenge for large-scale sensor networks, owing to its flexible installation and broad applicability. Structural parameter optimization is essential to enhance the power density of the FSMFEH. However, existing finite element simulation methods fail to account for the interdependencies among parameters, thereby impeding efficient identification of optimal solutions. These methods also suffer from strong subjectivity, limited generalizability, and is time-consuming. Although the empirical parameter search method based on the FSMFEH mathematical model reduces the optimization time, other issues remain unresolved. Therefore, a novel parameter optimization method for the FSMFEH based on an improved multi-objective particle swarm optimization (MOPSO) algorithm is proposed. First, the output power and power density models of the FSMFEH accounting for magnetic core saturation characteristics are derived, and a multi-objective parameter optimization model corresponding to the FSMFEH parameter optimization problem is formulated. Second, an improved MOPSO algorithm is developed based on a control parameter optimization strategy to enhance the comprehensive performance of the algorithm. Finally, the improved MOPSO algorithm is employed to solve the FSMFEH parameter optimization problem, and the optimal structural parameters are identified via a specific screening strategy. The results demonstrate that, compared with the finite element simulation method and the empirical parameter search method based on the FSMFEH mathematical model, the proposed method efficiently identifies optimal structural parameters under varying external magnetic field conditions, with convergence achieved within 70 iterations and a single optimization runtime within 60 seconds. The method exhibits strong generalizability and high computational efficiency.