  Optimization problems with more than one objective consist in a very
attractive topic for researchers due to its applicability in real-world
situations. Over the years, the research effort in the Computational
Intelligence field resulted in algorithms able to achieve good results by
solving problems with more than one conflicting objective. However, these
techniques do not exhibit the same performance as the number of objectives
increases and become greater than 3. This paper proposes an adaptation of the
metaheuristic Fish School Search to solve optimization problems with many
objectives. This adaptation is based on the division of the candidate solutions
in clusters that are specialized in solving a single-objective problem
generated by the decomposition of the original problem. For this, we used
concepts and ideas often employed by state-of-the-art algorithms, namely: (i)
reference points and lines in the objectives space; (ii) clustering process;
and (iii) the decomposition technique Penalty-based Boundary Intersection. The
proposed algorithm was compared with two state-of-the-art bio-inspired
algorithms. Moreover, a version of the proposed technique tailored to solve
multi-modal problems was also presented. The experiments executed have shown
that the performance obtained by both versions is competitive with
state-of-the-art results.
