An improved teaching–learning-based optimization algorithm with a modified learner phase and a new mutation-restarting phase

Abstract

Teaching–learning-based optimization (TLBO) is a powerful metaheuristic algorithm for solving complex optimization problems pertaining to the global optimum. Many TLBO variants have been presented to improve the local optima avoidance capability and to increase the convergence speed. In this study, a modified learner phase and a new gradient-based mutation strategy are proposed in an improved TLBO algorithm (TLBO-LM). A restarting strategy is adopted to help TLBO-LM escape from local optima and is combined with the gradient-based mutation strategy to build a mutation-restarting phase in an improved TLBO algorithm (LMRTLBO). The modified learner phase integrates a mutation operator to ensure the exploration capability and a dynamic boundary constraint strategy to increase the convergence speed. A greedy gradient information estimation method is developed to accelerate the convergence rate and then is combined with a mutation operator to establish the gradient-based mutation strategy with a good balance between exploration and exploitation. The proposed algorithms are examined by the CEC 2014 and 2020 benchmark functions. The results suggest that both the modified learner phase and gradient-based mutation strategy significantly enhance the exploration and exploitation capabilities of TLBO and that the restarting strategy effectively avoids local optima but sacrifices the exploitation capability to some extent. TLBO-LM and LMRTLBO obtain better results compared with ten algorithms, including TLBO variants, and are competitive compared with five CEC winners.