一种改进的多学习教与学优化算法

An Improved Multi-learning Teaching-learning-based Optimization Algorithm

针对传统教与学算法在解决复杂多峰函数优化问题时,具有局部最优且搜索开发能力较差的缺点,提出了一种改进的多学习教与学优化算法,新算法为学员的每一维加入不同的教学因子,设计了基于学员均值比较的教师选择策略和向教师及学员学习的多学习策略。基于多个单峰和多峰函数的仿真结果表明,新算法跟传统的、改进的教与学算法相比,在稳定性、寻优精度和收敛速度方面更具优势。

In order to solve the problem that the traditional teaching - learning - based optimization algorithm easily falls into local optimum and lacks search ability while dealing with the complex multimodal optimization, an improved multi - learning teaching - learning - based optimization algorithm is proposed. It adds different teaching factors to each of the students ^ dimensions, and de-signs a teacher selection mechanism based on the students^mean value and a multi - learning strategy learning both from the teacher and students. The experimental results obtained from testing a serial of unimodal and multimodal functions show that, compared with the traditional teaching - learning - based optimization algorithm and some other relative improved algorithms, the new algorithm has more advantages in stability, optimization precision and convergence speed.