During the past two decades, a variety of
multiobjective evolutionary algorithms (MOEAs) have been
proposed in the literature. As pointed out in some recent studies,
however, the performance of an MOEA can strongly depend on
the Pareto front shape of the problem to be solved, whereas most
existing MOEAs show poor versatility on problems with different shapes of Pareto fronts. To address this issue, we propose
an MOEA based on an enhanced inverted generational distance
indicator, in which an adaptation method is suggested to adjust
a set of reference points based on the indicator contributions
of candidate solutions in an external archive. Our experimental results demonstrate that the proposed algorithm is versatile
for solving problems with various types of Pareto fronts, outperforming several state-of-the-art evolutionary algorithms for
multiobjective and many-objective optimization.