XIE Jiacheng, XIONG Juxia, HE Zhenjiang
Accepted: 2024-08-30
Aimed at the problems of insufficient optimization performance and accuracy of SMA in solving wind farm layout optimization problem (WFLOP), and the slow convergence speed and premature convergence to local extreme values in SMA, an improved slime mold algorithm based on adaptive contraction and genetic learning strategy is proposed. First, a wind farm layout model is initially established based on the specific environmental conditions. Then, for the problem of premature convergence to local extreme values, a genetic learning strategy is introduced to enhance the convergence speed and global search ability of SMA, resulting in the GLSMA. Finally,Aimed at the problems of WFLOP, the maximum rule coding solution vector is adopted, and an adaptive contraction strategy is designed to update the position of slime moulds using the power generation of wind turbines, which improving the solution accuracy. The experimental results show compared to SMA, Grey Wolf Optimization (GWO), Salp Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), and Genetic Learning Particle Swarm Optimization (GLPSO), GLSMA has faster convergence speed and higher optimization accuracy in 19 test functions, and the A-GLSMA has higher performance than Genetic Algorithm (GA) in solving WFLOP under two wind direction distributions.