  This paper proposes a novel constraint-handling mechanism named angle-based
constrained dominance principle (ACDP) embedded in a decomposition-based
multi-objective evolutionary algorithm (MOEA/D) to solve constrained
multi-objective optimization problems (CMOPs). To maintain the diversity of the
working population, ACDP utilizes the information of the angle of solutions to
adjust the dominance relation of solutions during the evolutionary process.
This paper uses 14 benchmark instances to evaluate the performance of the
MOEA/D with ACDP (MOEA/D-ACDP). Additionally, an engineering optimization
problem (which is I-beam optimization problem) is optimized. The proposed
MOEA/D-ACDP, and four other decomposition-based CMOEAs, including C-MOEA/D,
MOEA/D-CDP, MOEA/D-Epsilon and MOEA/D-SR are tested by the above benchmarks and
the engineering application. The experimental results manifest that MOEA/D-ACDP
is significantly better than the other four CMOEAs on these test instances and
the real-world case, which indicates that ACDP is more effective for solving
CMOPs.
