具有自学机制和退火选择的教学优化算法

为了克服教学优化(TLBO)算法容易早熟,解精度低的弱点,提出一种具有教师自学和学生选择学习的改进教学优化算法。在每次迭代过程中教师个体首先通过反向学习(OBL),实现教师的自我提高,加强优秀个体周围邻域的搜索,引导算法向包含全局最优的解空间逼近,保证算法具有较好的平衡和探索能力。学生个体通过随机执行反向学习进行自学习,同时亦向教师个体进行学习,计算两种学习方法后的状态相对教师个体的突跳概率,并以此概率为基础进行轮盘赌产生子个体。通过在多个标准测试函数上的实验仿真并与相关的算法对比,结果表明所提出的改进算法具有更高的收敛速度和收敛精度。

An improved teaching learning based optimization with self study and simulated anneal

Concerning the problem that the teaching learning based optimization (TLBO) algorithm is easy to premature with low solution precision, we propose an improved TLBO algorithm with self study of teachers and optional study of students. In every iteration, individual teachers adopt the opposition based learning (OBL) to generate an opposition search population, and the search space of the algorithm is guided to approximate optimum space. This mechanism is helpful for improving the balance and exploring the ability of the TLBO. Every individual student executes OBL randomly and studies from teachers at the same time. For keeping the diversity of the population, we calculate the students' jumping probability to current teachers. We adopt the roulette mechanism to choose the individuals which will replace the parent individuals. Compared with related algorithms, the simulations on 11 classical benchmark functions show that the proposed algorithm has better convergence rate and accuracy for numerical optimization, and is suitable for solving high dimensional optimization problem.