基于反馈的精英教学优化算法

精英教学优化算法（Elitist teaching-learning-based optimization，ETLBO）是一种基于实际班级教学过程的新型优化算法. 本文针对ETLBO算法寻优精度低、稳定性差的问题，提出了反馈精英教学优化算法（Feedback ETLBO）. 在ETLBO算法的基础上，通过在学生阶段之后加入反馈阶段，增加了学生的学习方式，保持学生的多样性特性，提高算法的全局搜索能力. 同时，反馈阶段是选举成绩较差的学生与教师交流，使成绩较差的学生快速向教师靠拢，使算法进行局部精细搜索，提高算法的寻优精度. 对6个无约束及5个约束标准函数的测试结果表明，FETLBO算法与其他算法相比在寻优精度和稳定性上更具优势. 最后将FETLBO算法应用于拉压弹簧优化设计问题及0-1背包问题，取得了满意结果.

Elitist Teaching-learning-based Optimization Algorithm Based on Feedback

Elitist teaching-learning-based optimization (ETLBO) is a novel optimization algorithm based on the practical teaching-learning process of the class. In this paper, we propose a feedback elitist teaching-learning-based optimization (FETLBO) to solve the problem of low precision and poor stability of the ETLBO. Based on the ETLBO, a feedback phase is introduced at the end of the learner phase to increase the learning style and ensure the diversity of students so as to improve the algorithm's global search ability. Meanwhile, the feedback phase is for the slow students to communicate with the teacher and enables them to be close to the teacher quickly, so that the algorithm uses the fine local search and improves the precision. Six unconstrained and five constrained classic tests show that the FETLBO algorithm outperforms the other algorithms in precision and stability. Finally, the FETLBO algorithm is applied to the tension/compression spring design problem and the 0-1 knapsack problem, and obtains satisfactory results.