  This paper proposes a push and pull search (PPS) framework for solving
constrained multi-objective optimization problems (CMOPs). To be more specific,
the proposed PPS divides the search process into two different stages,
including the push and pull search stages. In the push stage, a multi-objective
evolutionary algorithm (MOEA) is adopted to explore the search space without
considering any constraints, which can help to get across infeasible regions
very fast and approach the unconstrained Pareto front. Furthermore, the
landscape of CMOPs with constraints can be probed and estimated in the push
stage, which can be utilized to conduct the parameters setting for
constraint-handling approaches applied in the pull stage. Then, a constrained
multi-objective evolutionary algorithm (CMOEA) equipped with an improved
epsilon constraint-handling is applied to pull the infeasible individuals
achieved in the push stage to the feasible and non-dominated regions. Compared
with other CMOEAs, the proposed PPS method can more efficiently get across
infeasible regions and converge to the feasible and non-dominated regions by
applying push and pull search strategies at different stages. To evaluate the
performance regarding convergence and diversity, a set of benchmark CMOPs is
used to test the proposed PPS and compare with other five CMOEAs, including
MOEA/D-CDP, MOEA/D-SR, C-MOEA/D, MOEA/D-Epsilon and MOEA/D-IEpsilon. The
comprehensive experimental results demonstrate that the proposed PPS achieves
significantly better or competitive performance than the other five CMOEAs on
most of the benchmark set.
