  The infeasible parts of the objective space in difficult many-objective
optimization problems cause trouble for evolutionary algorithms. This paper
proposes a reference vector based algorithm which uses two interacting engines
to adapt the reference vectors and to evolve the population towards the true
Pareto Front (PF) s.t. the reference vectors are always evenly distributed
within the current PF to provide appropriate guidance for selection. The
current PF is tracked by maintaining an archive of undominated individuals, and
adaptation of reference vectors is conducted with the help of another archive
that contains layers of reference vectors corresponding to different density.
Experimental results show the expected characteristics and competitive
performance of the proposed algorithm TEEA.
