基于曲线自适应和模拟退火的蝗虫优化算法  

Grasshopper optimization algorithm based on curve adaptive and simulated annealing

作　　者：李洋州 顾磊[1] Li Yangzhou;Gu Lei(School of Computer Science,Nanjing University of Posts&Telecommunications,Nanjing 210000,China)

机构地区：[1]南京邮电大学计算机学院软件学院网络空间安全学院

出　　处：《计算机应用研究》2019年第12期3637-3643,共7页Application Research of Computers

基　　金：国家自然科学基金资助项目(61302157)

摘　　要：针对蝗虫优化算法容易陷入局部极值点、收敛速度慢、精度较差等缺点,提出曲线自适应和模拟退火蝗虫优化算法。首先,引入曲线自适应代替蝗虫优化算法关键参数的线性自适应,提高了算法的全局搜索能力;其次,在此基础上引入模拟退火算法,对蝗虫算法的劣势解具有一定概率的接收,使算法具有跳出局部最优,实现全局最优的能力。自适应缩小模拟退火中蝗虫位置随机解的范围,有利于进一步提高蝗虫算法的开发能力。通过测试函数测试,实验结果表明,改进的新算法具有更好的求解质量和收敛速度。This paper proposed curve adaptive and simulation annealing grasshopper optimization algorithm to avoid the disadvantages that the grasshopper optimization algorithm was easy to fall into local optimum,slow convergence speed and poor search accuracy. First of all,this paper introduced the adaptive curve to replace the linear adaptation of the grasshopper optimization algorithm key parameter,which improved the global search ability of the algorithm. Then,on this basis,this paper introduced the simulated annealing algorithm,which had a certain probability of receiving the inferior solution of the grasshopper algorithm to jump out of local optimization and achieve global optimization. Adaptive reduction the range of random solutions of grasshopper position in simulated annealing was beneficial to further improve the ability of grasshopper algorithm’s exploitation. Through test function,the experimental results show that the improved new algorithm has higher quality and better convergence speed.

关 键 词：蝗虫优化算法 模拟退火算法 混合算法 自适应曲线 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]