关键词: 爬行动物搜索算法, 教与学优化算法, 二次插值的天牛须搜索算法, 透镜成像反向学习, 工程问题求解

Abstract: Reptile Search Algorithm (RSA) has strong global exploration ability, but its exploitation ability is relatively weak and it cannot converge well in the late stage of the iteration. To address the above issues, MHCS-RSA (Reptile Search Algorithm based on Multi-hunting Coordination Strategy) was proposed, which was combined with the teaching-learning-based optimization algorithm, the beetle antennae search algorithm based on quadratic interpolation and the lens opposite-based learning strategy. MHCS-RSA retained the position update formula of the hunting cooperation in the encircling phase (exploration) and hunting phase (exploitation) of the RSA. In the hunting phase, the hunting coordination integrated the learning phase of the teaching-learning-based optimization algorithm and the beetle antennae search based on quadratic interpolation, which performed position update to improve the exploitation ability and convergence ability of the algorithm. In addition, the lens opposite-based learning strategy was introduced to enhance the ability of jumping out of the local optima. Experimental results on CEC 2020 show that MHCS-RSA has good optimization, convergence and robustness. Finally, by solving the tension/compression spring design problem and the reducer design problem, the validity of MHCS-RSA is further verified in solving practical problems.

Key words: Reptile Search Algorithm (RSA), teaching-learning-based optimization algorithm, beetle antennae search algorithm based on quadratic interpolation, lens opposite-based learning, engineering problem solving