  In the field of evolutionary multi-objective optimization, the approximation
of the Pareto front (PF) is achieved by utilizing a collection of
representative candidate solutions that exhibit desirable convergence and
diversity. Although several multi-objective evolutionary algorithms (MOEAs)
have been designed, they still have difficulties in keeping balance between
convergence and diversity of population. To better solve multi-objective
optimization problems (MOPs), this paper proposes a Two-stage Evolutionary
Framework For Multi-objective Optimization (TEMOF). Literally, algorithms are
divided into two stages to enhance the search capability of the population.
During the initial half of evolutions, parental selection is exclusively
conducted from the primary population. Additionally, we not only perform
environmental selection on the current population, but we also establish an
external archive to store individuals situated on the first PF. Subsequently,
in the second stage, parents are randomly chosen either from the population or
the archive. In the experiments, one classic MOEA and two state-of-the-art
MOEAs are integrated into the framework to form three new algorithms. The
experimental results demonstrate the superior and robust performance of the
proposed framework across a wide range of MOPs. Besides, the winner among three
new algorithms is compared with several existing MOEAs and shows better
results. Meanwhile, we conclude the reasons that why the two-stage framework is
effect for the existing benchmark functions.
