  The major difficulty in Multi-objective Optimization Evolutionary Algorithms
(MOEAs) is how to find an appropriate solution that is able to converge towards
the true Pareto Front with high diversity. Most existing methodologies, which
have demonstrated their niche on various practical problems involving two and
three objectives, face significant challenges in the dependency of the
selection of the EA parameters. Moreover, the process of setting such
parameters is considered time-consuming, and several research works have tried
to deal with this problem. This paper proposed a new Multi-objective Algorithm
as an extension of the Hybrid Adaptive Evolutionary algorithm (HAEA) called
MoHAEA. MoHAEA allows dynamic adaptation of the application of operator
probabilities (rates) to evolve with the solution of the multi-objective
problems combining the dominance- and decomposition-based approaches. MoHAEA is
compared with four states of the art MOEAs, namely MOEA/D, pa$\lambda$-MOEA/D,
MOEA/D-AWA, and NSGA-II on ten widely used multi-objective test problems.
Experimental results indicate that MoHAEA outperforms the benchmark algorithms
in terms of how it is able to find a well-covered and well-distributed set of
points on the Pareto Front.
