Group Leader Dominated Teaching-Learning Based Optimization

Abstract

Teaching-learning based optimization (TLBO), inspired from the teaching-learning process in a classroom, is a newly developed population based algorithm. Except population size and maximum number of iteration, it does not require any specific parameters. TLBO consists of two modes of searching phase, teacher and learner phase. In this paper, every learner is assigned to at least one groups and, instead of a learner studied by interacting directly with other learners, group leader is responsible for raising up the member's knowledge, i.e., to explore for optimal solution. The idea is analog to group discussion in which group leader always dominate group discussion direction and performance. For simplicity, the proposed algorithm will be denoted as LTLBO. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with original TLBO and particle swarm optimization (PSO).