摘要
针对基本樽海鞘群智能优化算法的收敛速度慢、搜索精度低、容易陷入局部最优的缺点,提出了一种自适应正余弦搜索樽海鞘群优化算法。该算法引入正余弦搜索,以加强领导者位置更新速度,提升算法寻优速率;在跟随者位置更新公式中引入自适应权重因子,提高算法跳出局部最优的能力,并且提高了算法的收敛精度。使用所提出的算法对12个典型寻优测试函数进行寻优,并与其他群智能算法进行对比,将所提算法用于FCCU主分馏塔模型参数估计,仿真结果表明了该算法的有效性。
An adaptive sine and cosine search Salp swarm optimization algorithm was proposed in view of the shortcomings of the intelligent optimization algorithm of basic salp swarm,such as slow convergence speed,low search accuracy and easy to fall into local optimal.The algorithm introduces sine and cosine search to enhance the updating speed of leader position and the optimization speed of the algorithm.The adaptive weight factor is introduced into the follower position update formula to improve the ability of the algorithm to jump out of local optimum as well as the convergence accuracy of the algorithm.The proposed algorithm was used to optimize 12 typical test functions,and compared with other swarm intelligence algorithms.The proposed algorithm was used to estimate the parameters of the main fractionation tower model of FCCU.Simulation results prove that the proposed algorithm is effective.
作者
张梓嘉
苏成利
王宁
李平
ZHANG Zi-jia;SU Cheng-li;WANG Ning;LI Ping(School of Information and Control Engineering,Liaoning Shihua University,Fushun 113001,China;National Laboratory of Industrial Control Technology,Zhejiang University,Hangzhou 310027,China;School of Electronic and Information Engineering,University of Science and Technology Liaoning,Anshan 114051,China)
出处
《当代化工》
CAS
2022年第2期407-412,417,共7页
Contemporary Chemical Industry
基金
国家自然科学基金资助项目(项目编号:61703191,61803191)
辽宁省联合自然科学基金资助项目(项目编号:2019-KF-03-05)
高等学校创新人才支持计划项目(项目编号:LR2019037)。
