  Fitness landscape analysis investigates features with a high influence on the
performance of optimization algorithms, aiming to take advantage of the
addressed problem characteristics. In this work, a fitness landscape analysis
using problem features is performed for a Multi-objective Bayesian Optimization
Algorithm (mBOA) on instances of MNK-landscape problem for 2, 3, 5 and 8
objectives. We also compare the results of mBOA with those provided by NSGA-III
through the analysis of their estimated runtime necessary to identify an
approximation of the Pareto front. Moreover, in order to scrutinize the
probabilistic graphic model obtained by mBOA, the Pareto front is examined
according to a probabilistic view. The fitness landscape study shows that mBOA
is moderately or loosely influenced by some problem features, according to a
simple and a multiple linear regression model, which is being proposed to
predict the algorithms performance in terms of the estimated runtime. Besides,
we conclude that the analysis of the probabilistic graphic model produced at
the end of evolution can be useful to understand the convergence and diversity
performances of the proposed approach.
