  The development of efficient and effective evolutionary multi-objective
optimization (EMO) algorithms has been an active research topic in the
evolutionary computation community. Over the years, many EMO algorithms have
been proposed. The existing EMO algorithms are mainly developed based on the
final population framework. In the final population framework, the final
population of an EMO algorithm is presented to the decision maker. Thus, it is
required that the final population produced by an EMO algorithm is a good
solution set. Recently, the use of solution selection framework was suggested
for the design of EMO algorithms. This framework has an unbounded external
archive to store all the examined solutions. A pre-specified number of
solutions are selected from the archive as the final solutions presented to the
decision maker. When the solution selection framework is used, EMO algorithms
can be designed in a more flexible manner since the final population is not
necessarily to be a good solution set. In this paper, we examine the design of
MOEA/D under these two frameworks. We use an offline genetic algorithm-based
hyper-heuristic method to find the optimal configuration of MOEA/D in each
framework. The DTLZ and WFG test suites and their minus versions are used in
our experiments. The experimental results suggest the possibility that a more
flexible, robust and high-performance MOEA/D algorithm can be obtained when the
solution selection framework is used.
