  Existing studies on dynamic multi-objective optimization focus on problems
with time-dependent objective functions, while the ones with a changing number
of objectives have rarely been considered in the literature. Instead of
changing the shape or position of the Pareto-optimal front/set when having
time-dependent objective functions, increasing or decreasing the number of
objectives usually leads to the expansion or contraction of the dimension of
the Pareto-optimal front/set manifold. Unfortunately, most existing dynamic
handling techniques can hardly be adapted to this type of dynamics. In this
paper, we report our attempt toward tackling the dynamic multi-objective
optimization problems with a changing number of objectives. We implement a new
two-archive evolutionary algorithm which maintains two co-evolving populations
simultaneously. In particular, these two populations are complementary to each
other: one concerns more about the convergence while the other concerns more
about the diversity. The compositions of these two populations are adaptively
reconstructed once the environment changes. In addition, these two populations
interact with each other via a mating selection mechanism. Comprehensive
experiments are conducted on various benchmark problems with a time-dependent
number of objectives. Empirical results fully demonstrate the effectiveness of
our proposed algorithm.
