人群搜索和樽海鞘群的混合算法优化PID参数  

A Hybrid Algorithm Based on Seeker Optimization Algorithm and Salp Swarm Algorithm for PID Parameters Optimization

作　　者：段绍米 罗会龙[1] 刘海鹏[2] Duan Shaomi;Luo Huilong;Liu Haipeng(The Faculty of Civil Engineering and Mechanics,Kunming University of Science and Technology,Kunming 650500,China;The Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学建筑工程学院,云南昆明650500 [2]昆明理工大学信息工程与自动化学院,云南昆明650500

出　　处：《系统仿真学报》2022年第6期1230-1246,共17页Journal of System Simulation

基　　金：国家自然科学基金(51766005);云南省烟草公司科技项目(2019530000241019)。

摘　　要：为解决在优化全局时人群搜索优化算法(seeker optimization algorithm,SOA)容易过早收敛的问题,提出了一种新的基于人群搜索和樽海鞘群(salp swarm algorithm,SSA)的SOA-SSA混合算法。基于双种群进化策略,种群中的部分个体由人群搜索优化算法进化,其余个体由樽海鞘群算法进化。SOA和SSA的个体都使用信息共享机制实现协同进化,增加了种群的多样性,避免了算法过早收敛。实验结果表明:该算法在高维函数和PID参数优化方面都是可行的。与其他算法相比,SOA-SSA算法的收敛速度快、精度高、鲁棒性强,有更好的优化性能。Aiming at the premature convergence of seeker optimization algorithm(SOA)during optimizing the global problems,a new SOA-SSA hybrid algorithm based on seeker optimization algorithm and salp swarm algorithm(SSA)is proposed.The SOA-SSA algorithm is based on a double population evolution strategy,in which some individuals of the population are evolved by seeker optimization algorithm and the rest are evolved from salp swarm algorithm.The individuals in SOA and SSA both employ an information sharing mechanism to realize the coevolution.These strategies increase the diversity of the population and avoid the premature convergence.The experimental results show that the proposed algorithm can be used in both the high-dimensional cases and the PID control parameter optimization.Compared to the other eleven algorithms,the SOA-SSA has the higher,convergence speed,precision and robustness,and has a better optimization performance.

关 键 词：混合 人群搜索算法 樽海鞘群算法 函数优化 PID参数优化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TK232[自动化与计算机技术—计算机科学与技术]