融合模拟退火和自适应的天牛须搜索算法

Fusion Simulated Annealing and Adaptive Beetle Antennae Search Algorithm

天牛须搜索算法是一种新型单体智能优化算法,该算法在低维函数优化中收敛速度快并且具有全局寻优能力,但在多维函数中收敛速度慢,寻优精度较低。针对以上缺点,提出了一种融入模拟退火过程以及自适应因子的改进天牛须搜索算法。该算法在寻优阶段采用自适应因子加快收敛,再通过模拟退火过程概率性跳出局部最优。采用6个标准测试函数进行测试,并与天牛须搜索算法、天牛群算法和模拟退火算法进行对比。仿真结果表明,该算法在多维函数优化问题中有更好的寻优能力。

Beetle antennae search algorithm is a new type of single intelligent optimization algorithm.The algorithm has fast convergence speed and global optimization ability in low-dimensional function optimization,but for multi-dimensional functions,the convergence speed and optimization ability of the algorithm are relatively low.To overcome the above shortcomings,Fusion Simulated Annealing and Adaptive Beetle Antennae Search Algorithm(SABAS)that integrated the simulated annealing process and the adaptive factor are proposed.Specifically,the adaptive factor is used to accelerate the convergence of the algorithm at certain conditions,and the simulated annealing process is used to enable the algorithm to jump out of the local optimum.Six standard test functions are used for testing,and the proposed algorithm is compared with the BAS,BSAS and SA Algorithm.The simulation results indicate that SABAS has better optimization ability in multi-dimensional function optimization problems.