  In evolutionary multiobjective optimization, effectiveness refers to how an
evolutionary algorithm performs in terms of converging its solutions into the
Pareto front and also diversifying them over the front. This is not an easy
job, particularly for optimization problems with more than three objectives,
dubbed many-objective optimization problems. In such problems, classic
Pareto-based algorithms fail to provide sufficient selection pressure towards
the Pareto front, whilst recently developed algorithms, such as
decomposition-based ones, may struggle to maintain a set of well-distributed
solutions on certain problems (e.g., those with irregular Pareto fronts).
Another issue in some many-objective optimizers is rapidly increasing
computational requirement with the number of objectives, such as
hypervolume-based algorithms and shift-based density estimation (SDE) methods.
In this paper, we aim to address this problem and develop an effective and
efficient evolutionary algorithm (E3A) that can handle various many-objective
problems. In E3A, inspired by SDE, a novel population maintenance method is
proposed to select high-quality solutions in the environmental selection
procedure. We conduct extensive experiments and show that E3A performs better
than 11 state-of-the-art many-objective evolutionary algorithms in quickly
finding a set of well-converged and well-diversified solutions.
