Closed-loop teaching-learning-based optimization algorithm for global optimization

Abstract

Teaching-Learning-Based optimization (TLBO) algorithm is a new a teaching-learning process inspired optimization algorithm, and it searches for global optimum through two basic modes of the learning: (i)teacher phase and (ii)learner phase. To alleviate the slow convergence and premature problem of the TLBO, a closed-loop teaching-learning-based optimization (CLTLBO) algorithm has been presented in this paper. In the given closed-loop algorithm feedback information is incorporated into the teacher phase of the TLBO algorithm to imitate the phenomena that the teacher can discuss problems with learners, and fill vacancy leak for learners during tutorial hours. In additional, in learner phase grouping and brainstorming operators are used to simulate the interactive collaboration of the learners through group discussions, presentation, and brainstorming process among themselves. The experimental results show that the proposed algorithm not only improves the global optimization performance, but also quickens the convergence speed and obtains robust results with good quality, which indicates this CLTLBO algorithm is an effective approach for solving global optimization problems.