关键词: 教与学优化（TLBO）, 动态自适应, 学习因子, 动态随机搜索（DRS）

Abstract:

To overcome the problem of premature and low precision in Teaching and Learning Based Optimization（TLBO） algorithm, this paper proposes an improved TLBO with Dynamic Self-adapting Learning（DSLTLBO）. In the “teaching” phase of DSLTLBO, a factor based on the self-adaptive is introduced, which makes the current individual learn mainly from the best individual in the early stage and Maintain one’s own state in the later for keeping the diversity of population. Later stage in iteration, teacher individual excutes the Dynamic Random Searching（DRS）to improve its ablity of exploring the new solutions around them. Experiments on 10 classical Benchmark functions show that DSLTLBO  has better convergence speed and solution accuracy than TLBO, and is suitable for solving higher dimensional optimization problems.

Key words: Teaching and Learning Based Optimization（TLBO）, dynamic self-adaptive, learning factor, Dynamic Random Searching（DRS）