中北大学学报（自然科学版）2025，Vol.46Issue(1)：53-63,75,12.DOI:10.62756/jnuc.issn.1673-3193.2023.11.0026
融合Tent映射和模拟退火的改进鸽群优化算法
Improved Pigeon Swarm Optimization Algorithm Combining Tent Mapping and Simulated Annealing
张安玲1
作者信息
- 1. 长治学院 数学系,山西 长治 046011
- 折叠
摘要
Abstract
Aiming at the problems that pigeon swarm optimization algorithm is easy to fall into local optimum,low solution accuracy and the poor local searching ability,improved pigeon swarm optimization algorithm combining Tent mapping and simulated annealing was proposed.Firstly,Tent mapping was used to initialize the population to make the initial population distribution more uniform.Then,the simulated annealing algorithm was added after running the landmark operator of the pigeon swarm optimization algorithm.The simulated annealing algorithm can jump out of the local optimal solution with a certain probability and has the asymptotic convergence to improve the ability of global optimization.The experimental results on the performance of the algorithm based on 16 benchmark functions show that the convergence accuracy of Tent-PIO-SA algorithm is improved by an average of 10 orders of magnitude compared with PIO and Tent-PIO algorithms.Especially for Rosenbrock function,which is extremely difficult to optimize,the convergence accuracy of Tent-PIO-SA algorithm achieves on average 6 orders of magnitude higher than the recent classical algorithms LECUSSA,SCASL,CML-WOA and APN-WOA,and 7 orders of magnitude higher than TLPSO,SCA-DE algorithm.It is proved that the proposed Tent-PIO-SA algorithm has strong optimization ability.关键词
鸽群优化算法/模拟退火算法/Tent映射Key words
pigeon swarm optimization algorithm/simulated annealing algorithm/Tent mapping分类
计算机与自动化引用本文复制引用
张安玲..融合Tent映射和模拟退火的改进鸽群优化算法[J].中北大学学报（自然科学版）,2025,46(1):53-63,75,12.
