  In the past few decades, many multiobjective evolutionary optimization
algorithms (MOEAs) have been proposed to find a finite set of approximate
Pareto solutions for a given problem in a single run, each with its own
structure. However, in many real-world applications, it could be desirable to
have structure constraints on the entire optimal solution set, which define the
patterns shared among all solutions. The current population-based MOEAs cannot
properly handle such requirements. In this work, we make the first attempt to
incorporate the structure constraints into the whole solution set by a single
Pareto set model, which can be efficiently learned by a simple evolutionary
stochastic optimization method. With our proposed method, the decision-makers
can flexibly trade off the Pareto optimality with preferred structures among
all solutions, which is not supported by previous MOEAs. A set of experiments
on benchmark test suites and real-world application problems fully demonstrates
the efficiency of our proposed method.
