Intelligent Design of Street Lamp in Rural Areas Based on an Improved Genetic Algorithm

This study addresses the demand for more efficient streetlight designs in rural areas by introducing an improved genetic algorithm (GA) to optimize the geometry and placement of streetlight poles. Conventional GAs frequently suffer from premature convergence and becoming trapped in local optima, reducing their effectiveness. To mitigate these issues, this research integrates the genetic algorithm with Sequential Quadratic Programming (SQP), using the quasi-optimal solution generated by the GA as the initial input for the SQP, enhancing both accuracy and stability. The methodology includes developing a geometric model of streetlight poles utilizing point cloud data and extracting the centerline via the optimized GA-SQP approach. Additionally, the study examines the effects of random errors, gross errors, incomplete point cloud data, and centerline deviations on the algorithm's performance.