A comparative study of a teaching–learning-based optimization algorithm on multi-objective unconstrained and constrained functions

Multi-objective optimization is the process of simultaneously optimizing two or more conflicting objectives subject to certain constraints. real-life engineering designs often contain more than one conflicting objective function, which requires a multi-objective approach. in a single-objective optimization problem, the optimal solution is clearly defined, while a set of trade-offs that gives rise to numerous solutions exists in multi-objective optimization problems. each solution represents a particular performance trade-off between the objectives and can be considered optimal. in this paper, the performance of a recently developed teaching–learning-based optimization (tlbo) algorithm is evaluated against the other optimization algorithms over a set of multi-objective unconstrained and constrained test functions and the results are compared. the tlbo algorithm was observed to outperform the other optimization algorithms for the multi-objective unconstrained and constrained benchmark problems.