基于免疫选择和自适应权重的鲸鱼优化定位算法  

Whale optimal location algorithm based on immune selection and adaptive weight

作　　者：肖剑[1] 刘经纬 高凡 程鸿亮[1] 胡欣[2] XIAO Jian;LIU Jing-wei;GAO Fan;CHENG Hong-liang;HU Xin(School of Electronics and Control Engineering,Chang'an University,Xi'an 710064,China;School of Energy and Electrical Engineering,Chang'an University,Xi'an 710064,China)

机构地区：[1]长安大学电子与控制工程学院,西安710064 [2]长安大学能源与电气工程学院,西安710064

出　　处：《兰州大学学报（自然科学版）》2024年第4期501-508,共8页Journal of Lanzhou University(Natural Sciences)

基　　金：宁夏回族自治区重点研发计划项目(2022BEG03071);陕西省秦创原“科学家+工程师”队伍建设项目(2024QCY-KXJ-161)。

摘　　要：针对鲸鱼优化算法(WOA)在到达时间差和到达角度混合定位中存在后期迭代种群多样性减小,容易导致局部最优、定位精确度降低的问题,提出一种基于免疫选择和自适应权重的鲸鱼优化算法(IM-WOA).由最大似然估计法得到目标定位函数,为丰富种群多样性引入免疫机制,能够有效产生新的个体,从而避免种群陷入局部最优.通过将自适应惯性权重应用于个体位置的更新公式,实现算法全局探索能力和局部开发能力的平衡和协调,对经典基准函数和目标定位函数进行求解.结果表明,与WOA、AWOA、CSSOA、PIO、CASSA算法相比,IM-WOA算法对绝大多数基准函数的求解具有更高的精度、稳定性和定位精度.Aiming at the problems of the whale optimization algorithm(WOA)in the time difference of arrival location,such as the decrease of population diversity in the later iteration,which is easy to fall into local optimum and low location accuracy,a whale optimization algorithm was proposed based on immune selection and adaptive weight(IM-WOA).The maximum likelihood estimation method was used to get the target location function,and the immune mechanism was added to increase the diversity of the population,which could effectively generate new individuals and avoid the population falling into the local optimum.The adaptive inertia weight was introduced into the individual position update formula to better coordinate the global exploration and local development ability of the algorithm.It was used to solve the classical benchmark function and target location function and the experimental results showed that,compared with the WOA,AWOA,CSSOA,PIO and CASSA,IM-WOA algorithm had higher accuracy and stability for most benchmark functions,and it had a higher location accuracy.

关 键 词：鲸鱼优化算法 到达时间差 种群多样性 免疫机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]