  The main feature of large-scale multi-objective optimization problems (LSMOP)
is to optimize multiple conflicting objectives while considering thousands of
decision variables at the same time. An efficient LSMOP algorithm should have
the ability to escape the local optimal solution from the huge search space and
find the global optimal. Most of the current researches focus on how to deal
with decision variables. However, due to the large number of decision
variables, it is easy to lead to high computational cost. Maintaining the
diversity of the population is one of the effective ways to improve search
efficiency. In this paper, we propose a probabilistic prediction model based on
trend prediction model and generating-filtering strategy, called LT-PPM, to
tackle the LSMOP. The proposed method enhances the diversity of the population
through importance sampling. At the same time, due to the adoption of an
individual-based evolution mechanism, the computational cost of the proposed
method is independent of the number of decision variables, thus avoiding the
problem of exponential growth of the search space. We compared the proposed
algorithm with several state-of-the-art algorithms for different benchmark
functions. The experimental results and complexity analysis have demonstrated
that the proposed algorithm has significant improvement in terms of its
performance and computational efficiency in large-scale multi-objective
optimization.
