Optimal design of laser diode arrays for uniform illumination using hybrid particle swarm optimization

Laser diode (LD) arrays produce light unevenly during plant supplementation, leading to problems such as reduced photosynthetic efficiency and uneven plant growth. To alleviate these problems, we propose an improved particle swarm optimization algorithm (Tent-Lévy-varied PSO, TLVPSO) for the optimization and simulation of LD light source array layout. The algorithm introduces an improved Lévy flight strategy to enhance the global search capability while balancing the exploration and exploitation of the algorithm. Additionally, the Tent chaos factor, nonlinearly varying inertia weights, and learning factor are introduced to increase the randomness and diversity of the initial particles and to balance the global exploration capability and local evolution capability of the algorithm. The objective function of light uniformity is constructed, and the improved particle swarm algorithm is used to optimize the LD array to obtain the LD spacing under the optimal light uniformity. Finally, five different algorithms are used to test and compare the performance of the algorithms and validate them in crop irradiation scenarios. The experimental results show that the proposed algorithm outperforms the other algorithms in terms of convergence stability and is both efficient and robust in solving the LD array optimization problem. The practical effectiveness of the improved particle swarm algorithm in crop replenishment is demonstrated through simulation and field verification.