【作者】 赵奕鑫； 李清伟； 孟祥夏； 李胜；

【Author】 ZHAO Yi-xin;LI Qing-wei;MENG Xiang-xia;LI Sheng;Nanjing University of Science and Technology,School of Automation;The 28th Research Institute of China Electronics technology Group Corporation,Science and Technology on Information Systems Engineering Laboratory;

【机构】 南京理工大学自动化学院； 中国电子科技集团第二十八研究所信息系统工程重点实验室；

【摘要】 针对传统海鸥算法收敛速度缓慢、寻优精度较低,且存在易陷入局部最优的缺点,提出一种带局部搜索和反馈机制的改进海鸥算法。首先增设了对海鸥迁徙的位置更新的反馈函数来提高算法收敛速度和精度,其次进一步引入了迭代局部搜索策略,来增强算法局部搜索以及跳出局部最优的能力。通过基准函数的仿真,对传统算法及改进算法进行寻优性能测试,结果表明改进算法的收敛速度、精度都有明显提升,同时在算法稳定性方面也具有明显优势。更多还原

【Abstract】 The traditional seagull optimization algorithm has problems of slow convergence,low accuracy,and easily falling into local optima,the seagull optimization algorithm with iterated local search and feedback(ILSFSOA) is proposed to overcome these weaknesses.Firstly,operator feedback of migration is designed to improve the convergence and accuracy,and then the iterative local search strategy is introduced to enhance the local search ability.The Benchmarking functions are selected for performance testing of different algorithms.The results show that the convergence and accuracy of ILSFSOA is significantly improved,and it also has obvious advantages in terms of algorithm stability.更多还原