Teaching-Learning Based Algorithm for Numerical Dynamic Multi-Objective Optimization Problems

Teaching–Learning-Based Optimization (TLBO) algorithm has become an alternative optimization method in a great number of applications in different fields of engineering and science since it has been introduced in 2011. Teaching-learning-based optimization (TLBO) is a population-based metaheuristic examination algorithm stimulated by the teaching and learning procedure in a classroom environment. TLBO with its comparatively reasonable performances outperforms some of the well-known metaheuristics concerning constrained benchmark tasks, controlled mechanical schemes, and nonstop non-linear numerical optimization problems. In the TLBO algorithm’s variants, all the learners have an equal chance of receiving information from the teacher and from each other as learners by interacting with each other in the class. The Experimental results of the TLBO method are tested on the set of CEC2018 dynamic multi-objective optimization benchmark problems and the computed results show that TLBO offers promising outcomes with diverse dynamic features and changing environments compared with other algorithms. It works well with producing a good class of population when alterations happen for pursuing the influential Paretooptimal set efficiently for refining population conjunction and multiplicity. TLBO extracted improved or equal quality solutions compared to other evolutionary algorithms. It is a promising alternative for the solution of difficult dynamic multiobjective optimization problems.