class="kwd-title">Keywords: Evolutionary algorithms, Teaching–learning based optimization, Edge detection, Canny and Sobel operators class="head no_bottom_margin" id="ab010title">AbstractThis paper presents an improved Teaching Learning Based Optimization (TLO) and a methodology for obtaining the edge maps of the noisy real life digital images. TLO is a population based algorithm that simulates the teaching–learning mechanism in class rooms, comprising two phases of teaching and learning. The ‘Teaching Phase’ represents learning from the teacher and ‘Learning Phase’ indicates learning by the interaction between learners. This paper introduces a third phase denoted by “Avoiding Phase” that helps to keep the learners away from the worst students with a view of exploring the problem space more effectively and escaping from the sub-optimal solutions. The improved TLO (ITLO) explores the solution space and provides the global best solution. The edge detection problem is formulated as an optimization problem and solved using the ITLO. The results of real life and medical images illustrate the performance of the developed method.
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机译：<！-fig ft0-> <！-fig @ position =“ anchor” mode =文章f4-> <！-fig mode =“ anchred” f5-> <！-fig / graphic | fig / alternatives / graphic mode =“ anchored” m1-> class =“ kwd-title”>关键字：进化算法，基于教学的优化，边缘检测，Canny和Sobel运算符 class =“ head no_bottom_margin“ id =” ab010title“>摘要本文提出了一种改进的基于教学学习的优化（TLO）和一种用于获取嘈杂的现实生活数字图像的边缘图的方法。 TLO是一种基于人口的算法，可模拟教室中的教学机制，包括教学的两个阶段。 “教学阶段”表示向老师学习，“学习阶段”表示通过学习者之间的互动进行学习。本文介绍了以“回避阶段”表示的第三阶段，该阶段有助于使学习者远离最差的学生，从而更有效地探索问题空间并逃避次优解决方案。改进的TLO（ITLO）探索了解决方案空间，并提供了全球最佳解决方案。边缘检测问题被公式化为优化问题，并使用ITLO解决。现实生活和医学图像的结果说明了该方法的性能。
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