  This paper proposes a push and pull search method in the framework of
differential evolution (PPS-DE) to solve constrained single-objective
optimization problems (CSOPs). More specifically, two sub-populations,
including the top and bottom sub-populations, are collaborated with each other
to search global optimal solutions efficiently. The top sub-population adopts
the pull and pull search (PPS) mechanism to deal with constraints, while the
bottom sub-population use the superiority of feasible solutions (SF) technique
to deal with constraints. In the top sub-population, the search process is
divided into two different stages --- push and pull stages.An adaptive DE
variant with three trial vector generation strategies is employed in the
proposed PPS-DE. In the top sub-population, all the three trial vector
generation strategies are used to generate offsprings, just like in CoDE. In
the bottom sub-population, a strategy adaptation, in which the trial vector
generation strategies are periodically self-adapted by learning from their
experiences in generating promising solutions in the top sub-population, is
used to choose a suitable trial vector generation strategy to generate one
offspring. Furthermore, a parameter adaptation strategy from LSHADE44 is
employed in both sup-populations to generate scale factor $F$ and crossover
rate $CR$ for each trial vector generation strategy. Twenty-eight CSOPs with
10-, 30-, and 50-dimensional decision variables provided in the CEC2018
competition on real parameter single objective optimization are optimized by
the proposed PPS-DE. The experimental results demonstrate that the proposed
PPS-DE has the best performance compared with the other seven state-of-the-art
algorithms, including AGA-PPS, LSHADE44, LSHADE44+IDE, UDE, IUDE,
$\epsilon$MAg-ES and C$^2$oDE.
