Elitist Teaching-learning-based Optimization Algorithm Based on Feedback
Kun Yu · Acta Automatica Sinica · 2014
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.