  This paper proposes an improved epsilon constraint-handling mechanism, and
combines it with a decomposition-based multi-objective evolutionary algorithm
(MOEA/D) to solve constrained multi-objective optimization problems (CMOPs).
The proposed constrained multi-objective evolutionary algorithm (CMOEA) is
named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to
the ratio of feasible to total solutions (RFS) in the current population. In
order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with
two and three objectives is designed, having large infeasible regions (relative
to the feasible regions), and they are called LIR-CMOPs. Then the fourteen
benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four
other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR,
MOEA/D-CDP and C-MOEA/D. The experimental results indicate that MOEA/D-IEpsilon
is significantly better than the other four CMOEAs on all of the test
instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs
with large infeasible regions. Furthermore, a real-world problem, namely the
robot gripper optimization problem, is used to test the five CMOEAs. The
experimental results demonstrate that MOEA/D-IEpsilon also outperforms the
other four CMOEAs on this problem.
