基于布朗运动与梯度信息的交替优化算法  

Alternately optimizing algorithm based on Brownian movement and gradient information

作　　者：沙林秀[1] 聂凡 高倩 孟号 SHA Linxiu;NIE Fan;GAO Qian;MENG Hao(School of Electronic Engineering,Xi’an Shiyou University,Xi’an Shaanxi 710065,China)

机构地区：[1]西安石油大学电子工程学院,西安710065

出　　处：《计算机应用》2022年第7期2139-2145,共7页journal of Computer Applications

基　　金：陕西省科技攻关重点项目(2020GY⁃046);西安石油大学研究生创新与实践能力培养计划项目(YCS21212115)。

摘　　要：针对群智能优化算法在优化过程中容易陷入局部最优、种群多样性低以及高维函数优化困难的问题,提出一种基于布朗运动与梯度信息的交替优化算法(AOABG)。首先,采用全局、局部搜索交替的寻优策略,即在有变优趋势的范围内切换为局部搜索,有变劣趋势的范围内切换为全局搜索;然后,局部搜索引入基于梯度信息的均匀分布概率的随机游走,全局搜索引入基于最优解位置的布朗运动的随机游走。将所提出的AOABG与近三年的哈里斯鹰优化算法(HHO)、麻雀搜索算法(SSA)、特种部队算法(SFA)在10个测试函数上对比。当测试函数维数为2、10时,AOABG在10个测试函数上的100次最终优化结果的均值与均方差均优于HHO、SSA与SFA。当测试函数为30维时,除了HHO在Levy函数上的表现优于AOABG(两者优化结果均值处于同一数量级)外,AOABG在其他9个测试函数上表现最好,与上述算法相比,优化结果均值提升了4.64%~94.89%。实验结果表明,AOABG在高维函数优化中收敛速度更快、稳定性更好、精度更高。Aiming at the problems that swarm intelligence optimization algorithms are easy to fall into local optimum as well as have low population diversity in the optimization process and are difficult to optimize high-dimensional functions,an Alternately Optimizing Algorithm based on Brownian-movement and Gradient-information(AOABG)was proposed.First,a global and local alternately optimizing strategy was used in the proposed algorithm,which means the local search was switched in the range of getting better and the global search was switched in the range of getting worse.Then,the random walk of uniform distribution probability based on gradient information was introduced into local search,and the random walk of Brownian motion based on optimal solution position was introduced into global search.The proposed AOABG algorithm was compared with Harris Hawk Optimization(HHO),Sparrow Search Algorithm(SSA)and Special Forces Algorithm(SFA)on 10 test functions.When the dimension of test function is 2 and 10,the mean value and standard deviation of AOABG’s 100 final optimization results on 10 test functions are better than those of HHO,SSA and SFA.When the test function is 30-dimensional,except for Levy function where HHO performs better than AOABG but the mean value of the two is in the same order of magnitude,AOABG performs best on the other nine test functions with an increase of 4.64%-94.89%in the average optimization results compared with the above algorithms.Experimental results show that AOABG algorithm has faster convergence speed,better stability and higher accuracy in high-dimensional function optimization.

关 键 词：交替寻优策略 高维函数优化 收敛速度 布朗运动 梯度信息 

分 类 号：TP301.[自动化与计算机技术—计算机系统结构]