  In this work, a new multiobjective optimization algorithm called
multiobjective learner performance-based behavior algorithm is proposed. The
proposed algorithm is based on the process of transferring students from high
school to college. The proposed technique produces a set of non-dominated
solutions. To judge the ability and efficacy of the proposed multiobjective
algorithm, it is evaluated against a group of benchmarks and five real-world
engineering optimization problems. Additionally, to evaluate the proposed
technique quantitatively, several most widely used metrics are applied.
Moreover, the results are confirmed statistically. The proposed work is then
compared with three multiobjective algorithms, which are MOWCA, NSGA-II, and
MODA. Similar to the proposed technique, the other algorithms in the literature
were run against the benchmarks, and the real-world engineering problems
utilized in the paper. The algorithms are compared with each other employing
descriptive, tabular, and graphical demonstrations. The results proved the
ability of the proposed work in providing a set of non-dominated solutions, and
that the algorithm outperformed the other participated algorithms in most of
the cases.
