  Multi-modal multi-objective optimization is to locate (almost) equivalent
Pareto optimal solutions as many as possible. Some evolutionary algorithms for
multi-modal multi-objective optimization have been proposed in the literature.
However, there is no efficient method for multi-modal many-objective
optimization, where the number of objectives is more than three. To address
this issue, this paper proposes a niching indicator-based multi-modal multi-
and many-objective optimization algorithm. In the proposed method, the fitness
calculation is performed among a child and its closest individuals in the
solution space to maintain the diversity. The performance of the proposed
method is evaluated on multi-modal multi-objective test problems with up to 15
objectives. Results show that the proposed method can handle a large number of
objectives and find a good approximation of multiple equivalent Pareto optimal
solutions. The results also show that the proposed method performs
significantly better than eight multi-objective evolutionary algorithms.
