  Researches have shown difficulties in obtaining proximity while maintaining
diversity for many-objective optimization problems. Complexities of the true
Pareto front pose challenges for the reference vector-based algorithms for
their insufficient adaptability to the diverse characteristics with no priori.
This paper proposes a many-objective optimization algorithm with two
interacting processes: cascade clustering and reference point incremental
learning (CLIA). In the population selection process based on cascade
clustering (CC), using the reference vectors provided by the process based on
incremental learning, the nondominated and the dominated individuals are
clustered and sorted with different manners in a cascade style and are selected
by round-robin for better proximity and diversity. In the reference vector
adaptation process based on reference point incremental learning, using the
feedbacks from the process based on CC, proper distribution of reference points
is gradually obtained by incremental learning. Experimental studies on several
benchmark problems show that CLIA is competitive compared with the
state-of-the-art algorithms and has impressive efficiency and versatility using
only the interactions between the two processes without incurring extra
evaluations.
