MOOTLBO: a new multi-objective observer-teacher-learner-based optimization

Multi-objective problems are seen in most fields and represent challenges for researchers to solve them. Although a wide range of developed techniques has been provided to tackle these problems, the complexity of the proposed solutions requires the use of alternative algorithms. The innovation of this article is a multi-objective version of observer-teacher-learner-based optimization (OTLBO) called multi-objective observer-teacher-learner-based optimization (MOOTLBO), and an external archive has been employed to save the non-dominated Pareto-optimal solutions so far. This archive chooses the solutions using a leader selection strategy and a roulette wheel. Accordingly, a mutation operator was also added to the algorithm. To prove the effectiveness of the proposed algorithm, nineteen standard test functions were used and compared via MOPSO, MOGWO and MOMPA algorithms. The Taguchi-grey relational method was employed to adjust the parameters of the investigated algorithms. The ability of the proposed algorithm was investigated by seven metrics. The results show that MOOTLBO outperforms the other algorithms in fourteen of the nineteen test problems and produces viable results.