摘要
为解决教与学优化算法容易早熟收敛的问题,在原算法的基础上提出一种基于混合策略改进的教与学优化算法(Mixed Strategy Based Improved Teaching-Learning Based Optimization,M-SITLBO)。首先,利用Logistic-Tent混沌映射策略初始化种群,保证种群的多样性;其次,在教师和学生阶段分别引入黄金正弦算法和基于莱维飞行与对数螺旋线的搜索策略优化个体的位置更新公式,增强并平衡算法的全局和局部收敛性能;最后,设计仿真对其寻优性能进行测试,结果表明改进后的教与学优化算法寻优速度、精度以及稳定性显著提升,且具有较强跳出局部最优的能力。
In order to solve the problem of premature convergence,this paper proposes a Mixed Strategy Based Improved Teaching-Learning Based Optimization.Firstly,the Logistic-Tent chaotic mapping strategy was used to initialize the population to ensure the diversity of the population.Secondly,the Golden Sine Algorithm and the search strategy based on Levy flight and logarithmic spiral were introduced to optimize the individual position update formula in the teacher and student stage respectively to improve and equilibrate the global and local convergence performance of the algorithm.Finally,simulation experiments were designed to test its optimization performance.The results show that the optimization speed,accuracy and stability of the improved Teaching-Learning Based Optimization are significantly improved,and it has a strong ability to jump out of the local optimal.
作者
丁正生
丁姝予
文嘉豪
DING Zheng-sheng;DING Shu-yu;WEN Jia-hao(College of Science,Xi'an University of Science&Technology,Xi'an Shaanxi 710600,China)
出处
《计算机仿真》
2024年第8期331-337,共7页
Computer Simulation
基金
国家自然科学基金项目(11601417)。
关键词
基于优化的教与学
混沌映射
黄金正弦算法
莱维飞行
对数螺旋线
Teaching-learning based optimization
Chaotic mapping
Golden sine algorithm
Levy flight
Logarithmic spiral
