提高海鸥优化算法寻优能力的改进策略及其应用  

Improved Strategy and Its Application to the Optimization of Seagull Optimization Algorithm

作　　者：严爱军 胡开成[1,2] YANAijunn;HU Kaicheng(Faculty of Information Technology,Bejing University of Technology,Beijing 100124,China;Engineering Research Center of Digital Community,Ministry of Education,Beijing 100124,China;Bejing Laboratory for Urban Mass Transit,Beijing 100124,China)

机构地区：[1]北京工业大学信息学部,北京100124 [2]数字社区教育部工程研究中心,北京100124 [3]城市轨道交通北京实验室,北京100124

出　　处：《信息与控制》2022年第6期688-698,共11页Information and Control

基　　金：国家自然科学基金(61873009);北京市自然科学基金(4192009)。

摘　　要：针对海鸥优化算法(SOA)收敛速度慢、容易陷入局部最优等问题,提出3种提高SOA算法寻优能力的改进策略:对非线性收敛因子与螺旋系数进行改进,以改善全局与局部搜索的协调能力,加快收敛速度;通过拓展攻击行为与攻击角度,以并行搜索的方式提升局部寻优性能;引入动态反向学习,使算法快速跳出局部最优,优化全局搜索。基于马尔可夫过程分析了改进海鸥优化算法(ISOA)的收敛性。通过16个基准函数测试了ISOA算法的寻优性能,并将其应用于PID(proportional-integral-derivative)参数整定中,结果表明,提出的改进策略能显著提高SOA算法的收敛速度与求解精度,ISOA算法在参数优化领域具有较好的应用效果。To tackle the seagull optimization algorithm(SOA) issue, including a slow convergence speed and easily attaining its local optima, we propose three optimization strategies. We improve the nonlinear convergence factor and the spiral coefficient to further improve the global and local search coordination ability and accelerate the convergence speed. By expanding the attack behavior and angle, we improve the local optimization performance by a parallel search. Furthermore, we introduce dynamic reverse learning to avoid local optima and optimized the global search process. We analyze the convergence of the improved SOA(ISOA) based on a Markov process. Additionally, we test the optimization performance of ISOA using 16 benchmark functions and apply it to proportional-integral-derivative(PID) parameter tuning. The results indicate that the proposed strategies remarkably improves the convergence speed and solution precision of SOA and that ISOA is effective in parameter optimization.

关 键 词：海鸥优化算法 寻优能力 并行搜索 动态反向学习 PID参数整定 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]