Teaching-Learning-Based Optimization Algorithm Based on Cauchy Reverse and Cross Selection

Abstract

Teaching-learning-based optimization (TLBO) algorithm is a heuristic algorithm for solving optimization problems in various fields. TLBO algorithm imitates the classroom teaching process. In this paper, the basic TLBO algorithm is briefly introduced. Then, to solve the problem that the TLBO algorithm is not accurate and easy to fall into local optimum, a teaching-learning-based optimization algorithm on cauchy reverse and cross selection (TLBO - CRCS) is proposed. First, cauchy reverse learning is used to speed up the convergence of the algorithm. Secondly, the basic teaching-learning-based optimization algorithm is improved, the strategy of randomly selecting the “teaching” stage or “learning” stage is proposed, and improved crossover and selection strategies are introduced in the teaching and learning stage. Finally, 28 IEEE CEC2013 test set functions are selected and compared with the other four algorithms. Experimental results show that the proposed algorithm has the best overall performance.