融合多狩猎协调策略的爬行动物搜索算法

Reptile search algorithm based on multi-hunting coordination strategy

爬行动物搜索算法(RSA)具有较强的全局探索能力,但开发能力相对薄弱,在迭代后期无法较好地收敛。针对上述问题,综合教与学优化(TLBO)算法、二次插值的天牛须搜索(BAS)算法和透镜成像反向学习策略,提出一种融合多狩猎协调策略的爬行动物搜索算法(MHCS-RSA)。MHCS-RSA保留了RSA包围阶段(全局探索)和狩猎阶段(局部开发)中狩猎合作的位置更新公式,在狩猎阶段,将狩猎协调融合TLBO算法的学习阶段和二次插值的BAS进行位置更新,以增强算法的开发能力和收敛能力;此外,引入透镜成像反向学习策略以增强算法跳出局部最优的能力。在CEC 2020测试函数上的实验结果表明,MHCS-RSA具有良好的寻优能力、收敛能力以及鲁棒性。最后通过对拉力/压力弹簧设计问题和减速器设计问题的求解,进一步验证了MHCS-RSA求解实际问题的有效性。

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,combined with the Teaching-Learning-Based Optimization(TLBO)algorithm,the Beetle Antennae Search(BAS)algorithm based on quadratic interpolation and the lens opposite-based learning strategy,Reptile Search Algorithm based on Multi-Hunting Coordination Strategy(MHCS-RSA)was proposed.In MHCS-RSA,the position update formula of the hunting cooperation in the encircling phase(global exploration)and hunting phase(local exploitation)of RSA was retained.And in the hunting coordination of the hunting phase,the learning phase of TLBO algorithm and the BAS based on quadratic interpolation were integrated to perform position update in order to improve the exploitation ability and convergence ability of the algorithm.In addition,the lens opposite-based learning strategy was introduced to enhance the algorithm ability of jumping out of the local optimum.Experimental results on CEC 2020 test functions show that MHCS-RSA has good optimization,convergence abilities and robustness.By solving the tension/compression spring design problem and the speed reducer design problem,the validity of MHCS-RSA is further verified in solving practical problems.