Collective information-based teaching–learning-based optimization for global optimization

Teaching–learning-based optimization (TLBO) has been widely used to solve global optimization problems. However, the optimization problems in various fields are becoming more and more complex. The canonical TLBO is easy to be trapped in the local optimum when dealing with these problems. In this paper, a new TLBO algorithm with collective intelligence concept introduced is proposed, namely collective information-based TLBO (CIBTLBO). CIBTLBO uses the information from the top learners to form CITeachers and uses the neighborhood information of each learner to form NTeachers, and these teachers help other learners learn in the teacher phase. Furthermore, CITeacher also helps in the learner phase. To demonstrate superiority of the proposed algorithm, experiments on 28 benchmark functions from CEC2013 are carried out, and the benchmark functions are set to 10, 30, 50 and 100 dimensions, respectively. The results show that the proposed CIBTLBO algorithm outperforms the other previous related algorithms.